TWI626926B - System and method for monitoring patient with abnormal body temperature - Google Patents
System and method for monitoring patient with abnormal body temperature Download PDFInfo
- Publication number
- TWI626926B TWI626926B TW106102912A TW106102912A TWI626926B TW I626926 B TWI626926 B TW I626926B TW 106102912 A TW106102912 A TW 106102912A TW 106102912 A TW106102912 A TW 106102912A TW I626926 B TWI626926 B TW I626926B
- Authority
- TW
- Taiwan
- Prior art keywords
- image
- module
- body temperature
- temperature
- thermal image
- Prior art date
Links
Landscapes
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
本發明提供一種體溫異常個體偵測系統與方法,以偵測一區域內體溫異常的個體。此系統包括相連接之感測模組與處理模組。感測模組持續地產生此區域的熱影像、將熱影像劃分為多個網格影像,並判斷是否存在溫度超過門檻溫度的網格影像,最後根據超過門檻溫度的網格影像判斷出目標熱影像。處理模組產生目標熱影像之溫度對時間的關係圖,並據此判斷該區域內是否存在體溫異常的個體,且若是,則根據目標熱影像的溫度與目標熱影像中每個網格影像的座標發出警示訊息,以回報體溫異常之個體於此區域內的位置及其體溫。 The invention provides a body temperature abnormality detecting system and method for detecting an individual with abnormal body temperature in an area. The system includes a connected sensing module and a processing module. The sensing module continuously generates a thermal image of the area, divides the thermal image into a plurality of mesh images, and determines whether there is a mesh image whose temperature exceeds the threshold temperature, and finally determines the target heat according to the mesh image exceeding the threshold temperature. image. The processing module generates a temperature versus time graph of the target thermal image, and determines whether there is an individual with abnormal body temperature in the region, and if so, according to the temperature of the target thermal image and each mesh image in the target thermal image The coordinates send a warning message to report the location of the individual whose body temperature is abnormal in this area and its body temperature.
Description
本發明乃是關於一種偵測系統與方法,特別是指一種體溫異常個體偵測系統與方法,能夠偵測一區域內是否存在體溫異常的個體,且能回報該個體於此區域內的位置及該個體的影像。 The present invention relates to a detection system and method, and more particularly to a body temperature abnormality detecting system and method capable of detecting whether an individual having an abnormal body temperature in a region can report the position of the individual in the region and The image of the individual.
就目前常見的體溫偵測系統而言,多是以發燒疫情篩檢為主要目的。此類的體溫偵測系統多適用於人員流動較為頻繁與密集的區域,例如,機場大廳、醫院大廳、辦公大樓、百貨公司等。然而,此類的體溫偵測系統並無法確切地掌握體溫異常之個體於被偵測區域內的位置。另外,由於此類的體溫偵測系統均單純以熱影像作為篩檢的依據,因此即便於被偵測區域中發現有體溫異常的個體,也無從得知該個體的外貌甚至是其身份。 As far as the current common body temperature detection system is concerned, it is mostly the main purpose of screening for fever epidemic. Such body temperature detection systems are more suitable for areas where people flow more frequently and densely, such as airport halls, hospital halls, office buildings, department stores, and the like. However, such a body temperature detection system cannot accurately grasp the position of an individual with abnormal body temperature in the detected area. In addition, since such a body temperature detecting system uses thermal images alone as a basis for screening, even if an individual with abnormal body temperature is found in the detected area, the appearance and even the identity of the individual cannot be known.
本發明提供一種體溫異常個體偵測系統,用以偵測一區域內是否存在體溫異常的個體。此種體溫異常個體偵測系統包括感測模組與處理模組,其中處理模組連接於感測模組。感測模組包括影像擷取模組、影像解構模組與誤差排除模組。影像解構模組連接於影像擷取模組,誤差排除模組連接於影像解構模組。 The invention provides a body temperature abnormality detecting system for detecting whether an abnormal body temperature exists in an area. The body temperature abnormality detecting system comprises a sensing module and a processing module, wherein the processing module is connected to the sensing module. The sensing module includes an image capturing module, an image deconstructing module and an error eliminating module. The image deconstruction module is connected to the image capture module, and the error elimination module is connected to the image deconstruction module.
影像擷取模組用以持續地針對該區域產生熱影像。影像解構模組用以根據預先設定之座標系統將該區域的熱影像劃分為複數個網格影像,並分別地判斷熱影像中是否存在溫度超過門檻溫度的網格影像,其中熱影像的每個網格影像具有以該座標系統定位之座標。誤差排除模組根據超過門檻溫度的網格影像判斷出目標熱影像。 The image capture module is configured to continuously generate thermal images for the area. The image deconstruction module is configured to divide the thermal image of the region into a plurality of mesh images according to a preset coordinate system, and respectively determine whether there is a mesh image in the thermal image whose temperature exceeds the threshold temperature, wherein each of the thermal images The grid image has coordinates that are positioned by the coordinate system. The error elimination module determines the target thermal image based on the mesh image that exceeds the threshold temperature.
處理模組包括影像分析模組與判斷模組,其中判斷模組連接於影像分析模組。影像分析模組接收目標熱影像,以獲得該目標熱影像中各網格影像之溫度對時間的關係圖。接著,判斷模組根據該關係圖判斷該區域內是否存在體溫異常的個體,其中該關係圖顯示了目標熱影像的溫度是否隨時間上升。若影像分析模組判斷出該區域內存在體溫異常的個體,則判斷模組根據目標熱影像中每個網格影像的座標發出警示訊息,以回報體溫異常之個體於該區域內所在的位置。 The processing module includes an image analysis module and a determination module, wherein the determination module is connected to the image analysis module. The image analysis module receives the target thermal image to obtain a temperature versus time graph of each of the mesh images in the target thermal image. Next, the determining module determines whether there is an individual with abnormal body temperature in the area according to the relationship diagram, wherein the relationship diagram shows whether the temperature of the target thermal image rises with time. If the image analysis module determines that there is an individual whose body temperature is abnormal in the area, the determination module sends a warning message according to the coordinates of each grid image in the target thermal image to report the position of the individual whose body temperature is abnormal in the area.
於本發明所提供之體溫異常個體偵測系統的一實施例中,影像擷取模組更持續地針對該區域產生實際影像,每一實際影像對應於每一熱影像,且影像解構模組根據預先設定之座標系統將該區域的實際影像劃分為複數個網格影像,其中實際影像的每個網格影像具有以該座標系統定位之座標。此外,處理模組更包括特徵辨識模組。根據目標熱影像產生之時間以及目標熱影像中每個網格影像的座標,特徵辨識模組找出實際影像中對應的該些網格影像,並對該些網格影像進行特徵辨識。 In an embodiment of the body temperature abnormality detecting system provided by the present invention, the image capturing module continuously generates an actual image for the area, each actual image corresponds to each thermal image, and the image deconstructing module is configured according to The pre-set coordinate system divides the actual image of the area into a plurality of grid images, wherein each grid image of the actual image has coordinates that are positioned by the coordinate system. In addition, the processing module further includes a feature recognition module. The feature recognition module finds the corresponding mesh images in the actual image according to the time of the target thermal image generation and the coordinates of each mesh image in the target thermal image, and performs feature recognition on the mesh images.
於本發明所提供之體溫異常個體偵測系統的一實施例中,於特徵辨識模組找出實際影像中對應的該些網格影像後,特徵辨識模組將該些網格影像還原成三維影像,以針對五官特徵或顱骨特徵對此三維影像進行特徵辨識,以辨識出不同的個體。 In an embodiment of the body temperature abnormality detecting system provided by the present invention, after the feature recognition module finds the corresponding mesh images in the actual image, the feature recognition module restores the mesh images into three dimensions. The image is characterized by facial features or cranial features to identify different individuals.
本發明實施例另提供一種發燒個體偵測方法,適用於一種發燒個體偵測系統,用以偵測一區域內是否存在發燒的一個體。所適用的發燒個體偵測系統包括相連接之感測模組與處理模組。 The embodiment of the invention further provides a method for detecting a fever individual, which is suitable for a fever individual detection system for detecting whether a body has a fever in an area. The applicable individual detection system for fever includes a connected sensing module and a processing module.
感測模組包括影像擷取模組、影像解構模組與誤差排除模組,影像解構模組連接於影像擷取模組,且誤差排除模組連接於影像解構模組。處理模組包括影像分析模組與判斷模組,且判斷模組連接於影像分析模組。 The sensing module includes an image capturing module, an image deconstructing module and an error eliminating module, the image deconstructing module is connected to the image capturing module, and the error eliminating module is connected to the image deconstructing module. The processing module includes an image analysis module and a determination module, and the determination module is connected to the image analysis module.
此種體溫異常個體偵測方法包括:透過影像擷取模組,持續地針對該區域產生一熱影像;透過影像解構模組,根據預先設定之座標系統將該區域的熱影像劃分為複數個網格影像,其中熱影像的每個網格影像具有以該座標系統定位之座標;透過誤差排除模組,根據超過一門檻溫度的網格影像判斷出目標熱影像;透過影像分析模組,接收目標熱影像,以獲得目標熱影像中各網格影像之溫度對時間的關係圖;透過判斷模組,根據關係圖判斷該區域內是否存在體溫異常的個體,且若該區域內存在體溫異常的個體,根據目標熱影像中每個網格影像的座標發出警示訊息,以回報體溫異常之個體於該區域內所在的位置。 The method for detecting abnormal body temperature includes: generating a thermal image for the region through the image capturing module; and dividing the thermal image of the region into a plurality of meshes according to a preset coordinate system through the image deconstruction module; a grid image, wherein each grid image of the thermal image has a coordinate positioned by the coordinate system; the error elimination module is used to determine the target thermal image according to the grid image exceeding a threshold temperature; and the image is received through the image analysis module Thermal image to obtain the temperature versus time relationship of each grid image in the target thermal image; through the judgment module, determine whether there is an individual with abnormal body temperature in the region according to the relationship diagram, and if there is an abnormal body temperature in the region A warning message is sent according to the coordinates of each grid image in the target thermal image to report the position of the individual whose body temperature is abnormal in the area.
於本發明所提供之體溫異常個體偵測方法的一實施例中,體溫異常個體偵測方法更包括:透過影像擷取模組,持續地針對該區域產生實際影像,其中每一實際影像對應於每一熱影像;以及透過影像解構模組,根據預先設定之座標系統將該區域的實際影像劃分為複數個網格影像,其中實際影像的每個網格影像具有以該座標系統定位之座標。此外,於此實施例中,體溫異常個體偵測方法所適用的體溫異常個體偵測系統更包括特徵辨識模組,且體溫異常個體偵測方法更包括:透過特徵辨識模組,根據目標熱影像產生之時間以及目標熱影像中每個網格影像的座標,找出實際影像中對應的該些網格影像,並對該些網格影像進行特徵辨識。 In an embodiment of the method for detecting a body temperature abnormality according to the present invention, the method for detecting abnormal body temperature includes: generating an actual image for the region through the image capturing module, wherein each actual image corresponds to Each of the thermal images is divided into a plurality of mesh images according to a preset coordinate system, wherein each of the mesh images of the actual image has a coordinate positioned by the coordinate system. In addition, in this embodiment, the body temperature abnormality detecting system applied to the body temperature abnormality detecting method further includes a feature recognition module, and the body temperature abnormality individual detecting method further comprises: transmitting the feature recognition module according to the target heat image. The generated time and the coordinates of each mesh image in the target thermal image, find the corresponding mesh images in the actual image, and perform feature recognition on the mesh images.
於本發明所提供之體溫異常個體偵測方法的一實施例中,於找出該實際影像中對應的該些網格影像後,特徵辨識模組將該些網格影像還原成三維影像,以針對五官特徵或顱骨特徵對此三維影像進行特徵辨識。 In an embodiment of the method for detecting a body temperature abnormality according to the present invention, after the corresponding mesh images in the actual image are found, the feature recognition module restores the mesh images into a three-dimensional image, The three-dimensional image is characterized for facial features or skull features.
綜上所述,利用本發明所提供之體溫異常個體偵測系統與方法,便能夠自動地偵測出一區域內體溫異常症狀的個體,並且得知該個體目前位於此區域的哪個位置,以及獲得該個體的實際影像。由於體溫異常的症狀對於許多疾病來說係為一種警示症狀,若能及時發現將有利於疾病的診斷與治療。因此,透過本發明所提供之體溫異常個體偵測系統與方法,醫護人員就能先了解目前候診間內的所有人中是否有人具有體溫異常的症狀;此外,醫護人員還能進一步得知具有此種症狀的人其位置以及身份。如以一來,將有利於醫護人員對此人進行即時的照護。 In summary, the body temperature abnormality detecting system and method provided by the present invention can automatically detect an individual with abnormal temperature symptoms in an area, and know where the individual is currently located in the area, and Obtain the actual image of the individual. Symptoms of abnormal body temperature are a warning symptom for many diseases, and if found in time, it will be conducive to the diagnosis and treatment of the disease. Therefore, through the body temperature abnormality detecting system and method provided by the invention, the medical staff can firstly know whether any of the people in the waiting room have symptoms of abnormal body temperature; in addition, the medical staff can further know that there is The location of a person with symptoms and identity. In the first place, it will help medical staff to take immediate care of this person.
為使能更進一步瞭解本發明之特徵及技術內容,請參閱以下有關本發明之詳細說明與附圖,但是此等說明與所附圖式僅係用來說明本發明,而非對本發明的權利範圍作任何的限制。 The detailed description of the present invention and the accompanying drawings are to be understood by the claims The scope is subject to any restrictions.
10‧‧‧感測模組 10‧‧‧Sensing module
12‧‧‧影像擷取模組 12‧‧‧Image capture module
14‧‧‧影像解構模組 14‧‧‧Image Deconstruction Module
16‧‧‧誤差排除模組 16‧‧‧Error elimination module
20‧‧‧處理模組 20‧‧‧Processing module
22‧‧‧影像分析模組 22‧‧‧Image Analysis Module
24‧‧‧判斷模組 24‧‧‧Judgement module
26‧‧‧特徵辨識模組 26‧‧‧Character Identification Module
27‧‧‧位移檢索模組 27‧‧‧Displacement Search Module
28‧‧‧影像還原模組 28‧‧‧Image restoration module
T‧‧‧溫度回歸線 T‧‧‧Temperature regression line
A~E‧‧‧網格影像 A~E‧‧‧ Grid Image
S601~S607‧‧‧步驟 S601~S607‧‧‧Steps
S701~S703、S704A~S704B、S705A~S705B、S706A~S706B、S707~S708‧‧‧步驟 S701~S703, S704A~S704B, S705A~S705B, S706A~S706B, S707~S708‧‧‧ steps
圖1為根據本發明例示性實施例所繪示之體溫異常個體偵測系統之方塊圖。 1 is a block diagram of a body temperature abnormality detecting system according to an exemplary embodiment of the present invention.
圖2為將熱影像區分為多個網格影像的示意圖。 FIG. 2 is a schematic diagram of dividing a thermal image into a plurality of mesh images.
圖3為目標熱影像中各網格影像之溫度對時間的一關係圖。 Figure 3 is a graph of temperature vs. time for each grid image in the target thermal image.
圖4為根據本發明另一例示性實施例所繪示之體溫異常個體偵測系統之方塊圖。 FIG. 4 is a block diagram of a body temperature abnormality detecting system according to another exemplary embodiment of the present invention.
圖5A與圖5B分別顯示了針對一區域於同一時間所拍攝之熱影像與實際影像。 5A and 5B respectively show thermal images and actual images taken at the same time for a region.
圖6為根據本發明例示性實施例所繪示之體溫異常個體偵測方法的流程圖。 FIG. 6 is a flowchart of a method for detecting an abnormal body temperature according to an exemplary embodiment of the present invention.
圖7為根據本發明另一例示性實施例所繪示之體溫異常個體偵測方法的流程圖。 FIG. 7 is a flowchart of a method for detecting an abnormal body temperature according to another exemplary embodiment of the present invention.
在下文將參看隨附圖式更充分地描述各種例示性實施例,在隨附圖式中展示一些例示性實施例。然而,本發明概念可能以許多不同形式來體現,且不應解釋為限於本文中所闡述之例示性實施例。 Various illustrative embodiments are described more fully hereinafter with reference to the accompanying drawings. However, the inventive concept may be embodied in many different forms and should not be construed as being limited to the illustrative embodiments set forth herein.
確切而言,提供此等例示性實施例使得本發明將為詳盡且完整,且將向熟習此項技術者充分傳達本發明概念的範疇。 Rather, these exemplary embodiments are provided so that this invention will be in the
在諸圖式中,類似數字始終指示類似元件。 In the figures, like numerals are used to indicate like elements.
本發明提供之體溫異常個體偵測系統及其方法可自動地偵測出一區域內是否有個體具有體溫異常的症狀,並透過物聯網將資料傳遞至醫護人員端的裝置。 The body temperature abnormality detecting system and method provided by the invention can automatically detect whether an individual has symptoms of abnormal body temperature in an area and transmit the data to the device of the medical staff through the Internet of Things.
本發明提供之體溫異常個體偵測系統及其方法所適用之區域係為一小範圍的區域。舉例來說,本發明提供之體溫異常個體偵測系統及其方法適用於醫院的候診間…等。於以下的敘述中,將舉多個實施例來具體說明本發明提供之體溫異常個體偵測系統及其方法。 The area in which the body temperature abnormality detecting system and the method provided by the present invention are applied is a small area. For example, the body temperature abnormality detecting system and the method thereof provided by the present invention are applicable to a waiting room of a hospital, and the like. In the following description, a plurality of embodiments will be specifically described to describe a body temperature abnormality detecting system and a method thereof provided by the present invention.
〔體溫異常個體偵測系統的一實施例〕 [An embodiment of a body temperature abnormality detecting system]
請參照圖1,圖1為根據本發明例示性實施例所繪示之體溫異常個體偵測系統之方塊圖。如前述,本實施例所提供之體溫異常個體偵測系統所適用之區域係為一小範圍的區域,如:醫院的候診間,但本發明並不以此為限。 Please refer to FIG. 1. FIG. 1 is a block diagram of a body temperature abnormality detecting system according to an exemplary embodiment of the present invention. As described above, the area to which the body temperature abnormality detecting system provided in the present embodiment is applied is a small area, such as a waiting room of a hospital, but the invention is not limited thereto.
如圖1所示,本實施例所提供之體溫異常個體偵測系統係包括感測模組10與處理模組20。如圖1所示,感測模組10與處理模組20相連接。感測模組10主要包括影像擷取模組12、影像解構模組14 與誤差排除模組16,且處理模組20主要包括影像分析模組22與判斷模組24。 As shown in FIG. 1 , the body temperature abnormality detecting system provided in this embodiment includes a sensing module 10 and a processing module 20 . As shown in FIG. 1 , the sensing module 10 is connected to the processing module 20 . The sensing module 10 mainly includes an image capturing module 12 and an image deconstructing module 14 The error elimination module 16 and the processing module 20 mainly include an image analysis module 22 and a determination module 24.
影像解構模組14連接於影像擷取模組12,且誤差排除模組16連接於影像解構模組14。此外,影像分析模組22與判斷模組24相連接。須說明地是,於本實施例中,影像擷取模組12主要係以用以進行影像擷取的感測器硬體(如:熱像儀、攝像機…等),影像解構模組14與誤差排除模組16主要係以用以進行影像解構與誤差排除之韌體來實現(如:電腦…等),此外,影像分析模組22與判斷模組24則主要係以設置於雲端的伺服器設備來實現(如:雲端伺服器)。 The image deconstruction module 14 is connected to the image capture module 12 , and the error elimination module 16 is connected to the image deconstruction module 14 . In addition, the image analysis module 22 is connected to the determination module 24. It should be noted that, in this embodiment, the image capturing module 12 is mainly used as a sensor hardware for performing image capturing (eg, a thermal imager, a camera, etc.), and the image deconstructing module 14 is The error elimination module 16 is mainly implemented by a firmware for image deconstruction and error elimination (eg, computer, etc.), and the image analysis module 22 and the determination module 24 are mainly provided with a servo set in the cloud. Device implementation (eg, cloud server).
進一步說明,影像擷取模組12係用以持續地針對一區域產生熱影像。於本實施例中,影像擷取模組12至少包括有一熱像儀,來對被偵測區域持續地拍攝熱影像。於其他實施例中,考量到單一台熱像儀可能無法拍攝到整個被偵測區域,因此影像擷取模組12亦可包括多台熱像儀,以對整個被偵測區域進行完整的偵測。 Further, the image capturing module 12 is configured to continuously generate thermal images for an area. In this embodiment, the image capturing module 12 includes at least one thermal imager to continuously capture thermal images of the detected area. In other embodiments, it is considered that a single camera may not be able to capture the entire detected area. Therefore, the image capturing module 12 may also include multiple cameras to perform complete detection on the entire detected area. Measurement.
影像解構模組14係用以根據預先設定之一座標系統將一區域的熱影像劃分為複數個網格影像,以使誤差排除模組16接續地判斷每張熱影像中是否存在溫度超過一門檻溫度的網格影像。於本實施例中,影像解構模組14係以一個自訂的座標系統來將影像擷取模組12所拍攝的熱影像劃分成多個網格影像,並且此熱影像中的每個網格影像係以該座標系統之座標來進行定位。須說明地是,透過空間座標轉換的方式,每個網格影像於此座標系統中的座標均可轉換為實際空間中的位置資訊,此處關於座標轉換的細節應為本發明所屬技術領域中具有通常知識者所熟悉,故於此不多做描述。值得注意地是,前述門檻溫度係為攝氏37.5度,根據多數醫院臨床文獻的記載,攝氏37.5度為人體於健康狀態下的平均體溫。 The image deconstruction module 14 is configured to divide a thermal image of an area into a plurality of grid images according to a preset coordinate system, so that the error elimination module 16 successively determines whether there is a temperature exceeding one threshold in each thermal image. A grid image of temperature. In this embodiment, the image deconstruction module 14 divides the thermal image captured by the image capturing module 12 into a plurality of mesh images by a custom coordinate system, and each grid in the thermal image The image is positioned using the coordinates of the coordinate system. It should be noted that, by means of space coordinate conversion, the coordinates of each mesh image in the coordinate system can be converted into position information in real space, and the details about coordinate conversion should be in the technical field of the present invention. It is familiar to those with ordinary knowledge, so there is not much description here. It is worth noting that the threshold temperature is 37.5 degrees Celsius. According to the clinical literature of most hospitals, 37.5 degrees Celsius is the average body temperature of the human body in a healthy state.
誤差排除模組16係以根據超過門檻溫度的網格影像判斷出一目標熱影像。請參照圖2,圖2為將熱影像區分為多個網格影像的示意圖。如圖2所示,網格影像依據顏色的深淺度顯示高低不同的溫度,顏色較淺的網格影像所代表的溫度較低,反之,顏色較深的網格影像所代表的溫度較高。此外,顏色最深的網格影像即為超過門檻溫度的網格影像。也就是說,顏色最深的網格影像即可能被誤差排除模組16判斷為目標熱影像。 The error elimination module 16 determines a target thermal image based on a grid image that exceeds the threshold temperature. Please refer to FIG. 2. FIG. 2 is a schematic diagram of dividing a thermal image into a plurality of mesh images. As shown in FIG. 2, the grid image displays different temperatures according to the depth of the color, and the grid image with a lighter color represents a lower temperature. Conversely, the grid image with a darker color represents a higher temperature. In addition, the darkest grid image is a grid image that exceeds the threshold temperature. That is to say, the darkest grid image may be determined by the error elimination module 16 as the target thermal image.
接著將進一步說明誤差排除模組16由複數個網格影像中判斷書目標熱影像的細節。當誤差排除模組16根據超過門檻溫度的網格影像判斷目標熱影像時,誤差排除模組16係將複數個超過門檻溫度之相鄰網格影像作聯集(如圖2中的A處),並判斷A處即為目標熱影像。 Next, the error elimination module 16 is further described to determine the details of the book target thermal image from the plurality of grid images. When the error elimination module 16 determines the target thermal image based on the mesh image exceeding the threshold temperature, the error elimination module 16 associates a plurality of adjacent mesh images exceeding the threshold temperature (as shown at A in FIG. 2). And judge A is the target thermal image.
然而,被劃分為多個網格影像的熱影像中可能出現僅有單個網格影像其溫度超過門檻溫度的情形(如圖2中的B處),或者可能出現零星的幾個網格影像其溫度超過門檻溫度的情形(如圖2中的C處)。就體溫異常的個體於熱影像中的面積來看,前述兩種情況不可能為體溫異常的個體,也就是說,前述兩種情況無法被判斷為目標熱影像。 However, in a thermal image divided into multiple grid images, there may be a case where only a single grid image whose temperature exceeds the threshold temperature (as in B of FIG. 2), or several sporadic grid images may appear. The temperature exceeds the threshold temperature (as in Figure 2, C). In terms of the area of the individual with abnormal body temperature in the thermal image, the above two cases cannot be individuals with abnormal body temperature, that is, the above two cases cannot be judged as the target thermal image.
因此,於本實施例中,為了排除讓影像中並非由體溫異常之個體所顯示的熱點,超過門檻溫度之相鄰網格影像的數目必須大於等於一門檻數目。 Therefore, in the present embodiment, in order to exclude hot spots displayed by individuals who are not abnormal in body temperature in the image, the number of adjacent grid images exceeding the threshold temperature must be greater than or equal to the number of thresholds.
接下來,於誤差排除模組16判斷出目標熱影像後,此熱影像會由處理模組20中的影像分析模組22接收。根據所接收的熱影像,影像分析模組22便可獲得目標熱影像中各網格影像之溫度對時間的一關係圖,且根據此關係圖,判斷模組24便能判斷該區域內是否存在體溫異常的個體。舉例來說,請參照圖3,圖3即為目標熱影像中各網格影像之溫度對時間的一關係圖。 Next, after the error elimination module 16 determines the target thermal image, the thermal image is received by the image analysis module 22 in the processing module 20. According to the received thermal image, the image analysis module 22 can obtain a temperature versus time relationship of each mesh image in the target thermal image, and according to the relationship diagram, the determination module 24 can determine whether the region exists. Individuals with abnormal body temperature. For example, please refer to FIG. 3. FIG. 3 is a relationship diagram of temperature versus time of each mesh image in the target thermal image.
進一步說明,當判斷模組24根據目標熱影像中各網格影像之溫度對時間的關係圖來判斷該區域內是否存在體溫異常的個體時,判斷模組24會對該關係圖進行迴歸分析,以獲得一溫度回歸線(如圖3所示之溫度回歸線T)。接著,根據所獲得之溫度回歸線,判斷模組24便能判斷目標熱影像的溫度是否大於等於一警戒溫度。值得注意地是,此警戒溫度為攝氏38.5度。根據多數醫院臨床文獻的記載,當人體體溫介於攝氏37.5度至攝氏38.5度之間時,仍可視作人體處於健康狀態下;然而,當人體體溫超出攝氏38.5度時,則表示人體可能正處於於異常狀態下。 Further, when the judging module 24 determines whether there is an individual with abnormal body temperature in the region according to the temperature versus time graph of each grid image in the target thermal image, the judging module 24 performs regression analysis on the graph. Obtain a temperature regression line (temperature regression line T as shown in Figure 3). Then, based on the obtained temperature regression line, the determination module 24 can determine whether the temperature of the target thermal image is greater than or equal to a warning temperature. It is worth noting that this warning temperature is 38.5 degrees Celsius. According to the clinical literature of most hospitals, when the body temperature is between 37.5 degrees Celsius and 38.5 degrees Celsius, it can still be regarded as the human body in a healthy state; however, when the body temperature exceeds 38.5 degrees Celsius, it means that the human body may be in the body. In an abnormal state.
因此,若判斷模組24判斷目標熱影像的溫度大於等於攝氏38.5度,則可確定被偵測的區域內存在有體溫異常的個體。此時,判斷模組24便會根據目標熱影像的溫度與目標熱影像中每個網格影像的座標發出一警示訊息,以回報發燒之個體於此區域內所在的位置。也就是說,警示訊息可包含由目標熱影像於預設之座標系統所轉換出的實際空間中的座標,以及目標熱影像的溫度(即,體溫異常之個體的體溫)。 Therefore, if the determining module 24 determines that the temperature of the target thermal image is greater than or equal to 38.5 degrees Celsius, it can be determined that there is an individual with abnormal body temperature in the detected area. At this time, the judging module 24 sends a warning message according to the temperature of the target thermal image and the coordinates of each grid image in the target thermal image to report the position of the individual of the fever in this area. That is to say, the warning message may include coordinates in the actual space converted by the target thermal image in the preset coordinate system, and the temperature of the target thermal image (ie, the body temperature of the individual whose body temperature is abnormal).
須說明地是,復如圖3所示,前述溫度回歸線T係根據目標熱影像中各網格影像之溫度對時間的關係圖中一個時間區段(即圖3中的t-△t~t+△t)裡所記錄的溫度數據所獲得。 It should be noted that, as shown in FIG. 3, the temperature regression line T is a time segment according to the temperature versus time of each mesh image in the target thermal image (ie, t-Δt~t+ in FIG. 3). The temperature data recorded in Δt) was obtained.
因此,若根據溫度回歸線T,判斷模組24判斷目標熱影像的溫度並未大於等於攝氏38.5度,則表示該個體於時間區段(t-△t~t+△t)間雖具有較高的體溫,但則人體並非處於異常狀態下。於是,判斷模組24便不會針對此目標熱影像做出「被偵測的區域內存在有體溫異常的個體」之判斷。 Therefore, if the temperature of the target thermal image is not greater than or equal to 38.5 degrees Celsius according to the temperature regression line T, it means that the individual has a higher time zone (t-Δt~t+Δt). Body temperature, but the human body is not in an abnormal state. Therefore, the judging module 24 does not make a judgment on the target thermal image that "there is an individual with abnormal temperature in the detected area".
對於不同種類的疾病來說,其體溫異常的情形有其特徵,也就是說,由溫度隨著時間的變化來看,不同種類的疾病所引起的體溫異常症狀略有差異。因此,於本實施例中,判斷模組24可選擇進一步地將目標熱影像中各網格影像之溫度對時間的關係圖與 預先儲存的複數個參考圖作比較。須說明地是,不同的參考圖顯示了不同種類的疾病造成人體體溫異常時,人體體溫隨著時間的變化。 For different kinds of diseases, the abnormal temperature of the body has its characteristics, that is to say, from the change of temperature with time, the symptoms of abnormal body temperature caused by different kinds of diseases are slightly different. Therefore, in this embodiment, the determining module 24 can further select a relationship between the temperature of each mesh image in the target thermal image and time. A plurality of reference maps stored in advance are compared. It should be noted that different reference pictures show changes in body temperature over time when different types of diseases cause abnormal body temperature.
簡言之,判斷模組24除了能根據目標熱影像中各網格影像之溫度對時間的關係圖判斷出一區域內是否存在體溫異常的個體,判斷模組24還則能初步地判斷出可能是何種疾病造成該個體之體溫異常。 In short, the judging module 24 can determine whether there is an individual with abnormal body temperature in an area according to the temperature versus time map of each grid image in the target thermal image, and the judging module 24 can initially determine the possibility. What kind of disease causes the body temperature abnormality of the individual.
承上述,若目標熱影像中各網格影像之溫度對時間的關係圖與複數個參考圖之一相符,則判斷模組24判斷此區域內存在體溫異常的個體,並根據目標熱影像的溫度、目標熱影像中每個網格影像的座標以及比較出相符的參考圖發出一警示訊息。須說明地是,前述警示訊息可包含由目標熱影像於預設之座標系統所轉換出的實際空間中的座標、目標熱影像的溫度(即,體溫異常之個體的體溫)以及體溫異常的個體可能患有的疾病。 According to the above, if the temperature versus time graph of each grid image in the target thermal image matches one of the plurality of reference maps, the judging module 24 judges that there is an individual whose body temperature is abnormal in the region, and according to the temperature of the target thermal image. The coordinates of each grid image in the target thermal image and the matching reference maps send a warning message. It should be noted that the warning message may include the coordinates in the actual space converted by the target thermal image in the preset coordinate system, the temperature of the target thermal image (ie, the body temperature of the individual whose body temperature is abnormal), and the individual whose body temperature is abnormal. Possible diseases.
如此一來,本實施例所提供之體溫異常個體偵測系統便能在候診階段協助醫護人員獲知後診間內是否有體溫異常的個體,還能初步地了解該個體可能患有的疾病。 In this way, the abnormal body temperature detection system provided by the embodiment can assist the medical staff to know whether there is an abnormal temperature in the post-diagnosis room during the waiting period, and can also initially understand the diseases that the individual may have.
為了更具體地說明本發明所述之體溫異常個體偵測系統,以下將再舉一實施例來作更進一步的說明。於接下來的實施例中,將描述不同於上述圖1所繪示之實施例的部分,且其餘省略部分與上述圖1所繪示之實施例相同。此外,為說明便利起見,相似之參考數字或標號指示相似之元件。 In order to more specifically describe the body temperature abnormality detecting system of the present invention, an embodiment will be further described below. In the following embodiments, portions different from the embodiment illustrated in FIG. 1 above will be described, and the remaining omitted portions are the same as the embodiment illustrated in FIG. 1 described above. In addition, for the sake of convenience, like reference numerals or numerals indicate similar elements.
〔體溫異常個體偵測系統的另一實施例〕 [Another embodiment of a body temperature abnormality detecting system]
請參照圖4,圖4為根據本發明另一例示性實施例所繪示之體溫異常個體偵測系統之方塊圖。如前述實施例,本實施例所提供之體溫異常個體偵測系統所適用之區域同樣為一小範圍的區域,如:醫院的候診間,但本發明並不以此為限。 Please refer to FIG. 4. FIG. 4 is a block diagram of a body temperature abnormality detecting system according to another exemplary embodiment of the present invention. As in the foregoing embodiment, the area applicable to the body temperature abnormality detecting system provided by the embodiment is also a small area, such as a waiting room of a hospital, but the invention is not limited thereto.
本實施例所提供之體溫異常個體偵測系統與前述實施例所提供之體溫異常個體偵測系統具有類似的系統架構與相近的工作機制。惟,於系統架構上,本實施例所提供之體溫異常個體偵測系統與前述實施例所提供之體溫異常個體偵測系統的差異之一在於,於本實施例中,處理模組20更包括了特徵辨識模組26、位移檢索模組27與影像還原模組28。如圖4所示,特徵辨識模組26、位移檢索模組27與影像還原模組28均連接於判斷模組24。須說明地是,影像擷取模組12主要係以用以進行影像擷取的感測器硬體(如:熱像儀、攝像機…等),影像解構模組14與誤差排除模組16主要係以用以進行影像解構與誤差排除之韌體來實現(如:電腦…等),此外,影像分析模組22、判斷模組24特徵辨識模組26、位移檢索模組27與影像還原模組28則主要係以設置於雲端的伺服器設備來實現(如:雲端伺服器)。 The body temperature abnormality detecting system provided by the embodiment has similar system architecture and similar working mechanism as the body temperature abnormality detecting system provided by the foregoing embodiment. However, in the system architecture, one of the differences between the body temperature abnormality detecting system provided in this embodiment and the body temperature abnormality detecting system provided by the foregoing embodiment is that, in this embodiment, the processing module 20 further includes The feature recognition module 26, the displacement search module 27 and the image restoration module 28 are provided. As shown in FIG. 4, the feature recognition module 26, the displacement search module 27, and the image restoration module 28 are both connected to the determination module 24. It should be noted that the image capturing module 12 is mainly used for sensor hardware (such as a thermal imager, a camera, etc.) for image capturing, and the image deconstructing module 14 and the error eliminating module 16 are mainly It is implemented by firmware for image deconstruction and error elimination (eg, computer, etc.). In addition, image analysis module 22, determination module 24 feature recognition module 26, displacement retrieval module 27 and image restoration mode Group 28 is mainly implemented by a server device installed in the cloud (for example, a cloud server).
此外,於工作機制上,本實施例所提供之體溫異常個體偵測系統與前述實施例所提供之體溫異常個體偵測系統的其一差異在於,於產生熱影像的同時,於本實施例中,影像擷取模組12也持續地針對一區域產生一實際影像。也就是說,於本實施例中,影像擷取模組12至少包括有一熱像儀與一個一般的攝影裝置,來對被偵測區域持續地拍攝熱影像與實際影像,其中攝影裝置所拍攝的每一實際影像係對應於熱像儀所拍攝之每一熱影像。 In addition, in the working mechanism, the body temperature abnormality detecting system provided by the embodiment is different from the body temperature abnormality detecting system provided by the foregoing embodiment in that, in the present embodiment, in the present embodiment, The image capture module 12 also continuously generates an actual image for an area. That is, in the embodiment, the image capturing module 12 includes at least a thermal imager and a general imaging device for continuously capturing thermal images and actual images on the detected area, wherein the imaging device captures Each actual image corresponds to each thermal image captured by the camera.
於本實施例中,影像解構模組14係用以根據預先設定之一座標系統將一區域的熱影像與實際影像分別地劃分為複數個網格影像。同樣地,於本實施例中,影像解構模組14係以一個自訂的座標系統來將影像擷取模組12所拍攝的熱影像與實際影像分別地劃分成多個網格影像,並且將該熱影像與該實際影像中的每個網格影像係以該座標系統之座標來進行定位。須說明地是,透過空間座標轉換的方式,該熱影像與該實際影像中的每個網格影像於此座標系統中的座標均可轉換為實際空間中的位置資訊,此處關於 座標轉換的細節應為本發明所屬技術領域中具有通常知識者所熟悉,故於此不多做描述。 In the embodiment, the image deconstruction module 14 is configured to respectively divide a thermal image of an area and an actual image into a plurality of mesh images according to a preset coordinate system. Similarly, in the embodiment, the image deconstruction module 14 divides the thermal image and the actual image captured by the image capturing module 12 into a plurality of mesh images by using a customized coordinate system, and The thermal image and each of the grid images in the actual image are positioned with coordinates of the coordinate system. It should be noted that, by means of spatial coordinate conversion, the coordinates of the thermal image and each of the mesh images in the actual image can be converted into position information in real space, where The details of the coordinate conversion should be familiar to those of ordinary skill in the art to which the present invention pertains, and thus will not be described herein.
特徵辨識模組26與影像還原模組28的工作機制為本實施例所提供之體溫異常個體偵測系統與前述實施例所提供之體溫異常個體偵測系統於工作機制上的另一差異。 The working mechanism of the feature recognition module 26 and the image restoration module 28 is another difference in the working mechanism of the body temperature abnormality detecting system provided by the embodiment and the body temperature abnormality detecting system provided by the foregoing embodiment.
請參照圖5A與圖5B,圖5A與圖5B分別顯示了針對一區域於同一時間所拍攝之熱影像與實際影像。於本實施例中,當誤差排除模組16判斷出目標熱影像(如圖5A所示之D處)時,目標熱影像會由處理模組20中的影像分析模組22接收;同時,根據對應目標熱影像之熱影像被拍攝之時間以及目標熱影像中每個網格影像的座標,特徵辨識模組26會找出相同時間被拍攝之實際影像中對應的該些網格影像(如圖5B所示之E處),以對該些網格影像進行特徵辨識。 Referring to FIG. 5A and FIG. 5B, FIG. 5A and FIG. 5B respectively show thermal images and actual images taken at the same time for a region. In this embodiment, when the error elimination module 16 determines the target thermal image (as shown in FIG. 5A), the target thermal image is received by the image analysis module 22 in the processing module 20; Corresponding to the time when the thermal image of the target thermal image is captured and the coordinates of each mesh image in the target thermal image, the feature recognition module 26 finds the corresponding mesh images in the actual image captured at the same time (as shown in the figure). Feature E at 5B) to characterize the mesh images.
於本實施例中,由於特徵辨識模組26對該些網格影像所進行的特徵辨識為人物的特徵辨識,因此特徵辨識模組26會先將該些網格影像還原成一三維影像,以根據此三維影像進行特徵辨識。舉例來說,特徵辨識模組26會優先針對五官特徵來對此三維影像進行特徵辨識,然若特徵辨識模組26由此三維影像中辨識不出五官特徵,特徵辨識模組26便會接著針對顱骨特徵來對此三維影像進行特徵辨識。然而,此處僅為了例示說明特徵辨識模組26對三維影像進行特徵辨識的方式,並非用以限制本發明。根據辨識出的生物特徵(如:五官特徵或顱骨特徵),判斷模組24便能由一資料庫(未圖示)中比對出該個體的身分。須說明地是,此資料庫儲存有多筆身分資訊與其對應之生物特徵,並可為建置於本實施例之體溫異常個體偵測系統中的內建資料庫,或者可為一外部資料庫,例如,本實施例之體溫異常個體偵測系統可與各大醫療院所的病歷資料庫連接。 In this embodiment, since the feature recognition module 26 recognizes the feature of the mesh image as the feature recognition of the character, the feature recognition module 26 first restores the mesh image to a three-dimensional image to This 3D image is characterized. For example, the feature recognition module 26 preferentially identifies the three-dimensional image for the facial features. However, if the feature recognition module 26 does not recognize the facial features in the three-dimensional image, the feature recognition module 26 will then target The skull features to characterize this three-dimensional image. However, the manner in which the feature recognition module 26 performs feature recognition on the three-dimensional image is merely illustrated herein, and is not intended to limit the present invention. Based on the identified biometric features (e.g., facial features or skull features), the decision module 24 can compare the identity of the individual from a database (not shown). It should be noted that the database stores a plurality of identity information and corresponding biometrics, and may be a built-in database built in the body temperature abnormality detecting system of the embodiment, or may be an external database. For example, the body temperature abnormality detecting system of the present embodiment can be connected to the medical record database of each medical institution.
再者,考量到若該個體係因身體不適而出現體溫異常的情形,則合理地該個體於短時間內無法移動太大的距離,且可理解地,該個體的移動路徑具有連續性。。因此,於本實施例中,位移檢索模組27會利用預先設定之該座標系統來對目標熱影像於該區域內經過一預設時間後的位移距離作紀錄。接著,由判斷模組24判斷該位移距離內是否存在特徵辨識模組26所判定之具有人物特徵的目標熱影像。進一步地,若目標熱影像於該區域內經過該預設時間後的位移超過該預設距離,則判斷模組24便判斷此目標熱影像並非是體溫異常之個體的影像。於是,處理模組20便停止對此目標熱影像之相關資料與數據進行處理。 Furthermore, considering the situation in which the body temperature abnormality occurs due to physical discomfort, it is reasonable that the individual cannot move too large a distance in a short time, and understandably, the individual's moving path has continuity. . Therefore, in the embodiment, the displacement retrieval module 27 records the displacement distance of the target thermal image in the region after a predetermined time by using the coordinate system set in advance. Next, the determining module 24 determines whether there is a target thermal image having a character feature determined by the feature recognition module 26 within the displacement distance. Further, if the displacement of the target thermal image in the region after the preset time exceeds the preset distance, the determining module 24 determines that the target thermal image is not an image of an individual whose body temperature is abnormal. Therefore, the processing module 20 stops processing the related data and data of the target thermal image.
除此之外,為了能夠獲知體溫異常之個體當前的外貌,以便於醫護人員於候診間內快速地將其找尋,於本實施例中,影像還原模組28會將特徵辨識模組26所找出之實際影像中的該些網格影像進行影像還原,以產生體溫異常之個體當前的影像。也就是說,於本實施例中,警示訊息除了可包含由目標熱影像於預設之座標系統所轉換出的實際空間中的座標、目標熱影像的溫度(即,體溫異常個體的體溫)以及體溫異常的個體可能患有的疾病之外,警示訊息還可包含體溫異常之個體的身分,以及其當前的外貌。 In addition, in order to be able to find out the current appearance of the individual with abnormal body temperature, so that the medical staff can quickly find it in the waiting room, in this embodiment, the image restoration module 28 will find the feature recognition module 26 The mesh images in the actual image are subjected to image restoration to generate an image of the individual whose body temperature is abnormal. That is to say, in this embodiment, the warning message may include the coordinates in the actual space converted by the target thermal image in the preset coordinate system, the temperature of the target thermal image (ie, the body temperature of the abnormal body temperature), and In addition to the diseases that individuals with abnormal body temperature may have, the warning message may also include the identity of the individual with abnormal body temperature and its current appearance.
接下來,於以下的敘述中將以多個實施例說明可於圖1與圖4所繪示之體溫異常個體偵測系統中執行的體溫異常個體偵測方法,故請一併照圖1-4與圖5A-5B以利理解。 In the following description, the body temperature abnormality detecting method which can be executed in the body temperature abnormality detecting system which can be illustrated in FIG. 1 and FIG. 4 will be described in the following description, so please refer to FIG. 4 is understood in conjunction with Figures 5A-5B.
〔體溫異常個體偵測方法的一實施例〕 [An embodiment of a method for detecting abnormal body temperature]
請參照圖6,圖6為根據本發明例示性實施例所繪示之體溫異常個體偵測方法的流程圖。 Please refer to FIG. 6. FIG. 6 is a flowchart of a method for detecting an abnormal body temperature according to an exemplary embodiment of the present invention.
本實施例所提供之體溫異常個體偵測方法可適用於前述圖1所繪示之體溫異常個體偵測系統。復請參照圖1,前述圖1所繪示之體溫異常個體偵測系統係包括感測模組10與處理模組20。如圖1所示,感測模組10與處理模組20相連接。感測模組10主要包括影 像擷取模組12、影像解構模組14與誤差排除模組16,且處理模組20主要包括影像分析模組22與判斷模組24。影像解構模組14連接於影像擷取模組12,且誤差排除模組16連接於影像解構模組14。此外,影像分析模組22與判斷模組24相連接。本實施例所提供之體溫異常個體偵測方法則可描述如以下之步驟,即如圖6所示。 The body temperature abnormality detecting method provided in this embodiment can be applied to the body temperature abnormality detecting system shown in FIG. 1 . Referring to FIG. 1 , the body temperature abnormality detecting system illustrated in FIG. 1 includes the sensing module 10 and the processing module 20 . As shown in FIG. 1 , the sensing module 10 is connected to the processing module 20 . The sensing module 10 mainly includes a shadow The image capturing module 12 and the image determining module 24 and the determining module 24 are mainly included in the image capturing module 22 and the error removing module 16 . The image deconstruction module 14 is connected to the image capture module 12 , and the error elimination module 16 is connected to the image deconstruction module 14 . In addition, the image analysis module 22 is connected to the determination module 24. The body temperature abnormality detecting method provided in this embodiment can describe the following steps, as shown in FIG. 6.
於步驟S601中,透過影像擷取模組12持續地針對一區域產生一熱影像。於步驟S602中,透過影像解構模組14,根據預先設定之一座標系統將該區域的熱影像劃分為複數個網格影像,其中該熱影像的每個網格影像具有以該座標系統定位之一座標。於步驟S603中,透過誤差排除模組16,根據超過一門檻溫度的網格影像判斷出一目標熱影像。於步驟S604中,透過影像分析模組22,接收該目標熱影像,並產生目標熱影像中各網格影像之溫度對時間的一關係圖。於步驟S605中,透過判斷模組24,根據前述關係圖判斷該區域內是否存在體溫異常的個體。若判斷模組24未判斷出該區域內存在體溫異常的個體,則回到步驟S603,繼續判斷目標熱影像;然若判斷模組24判斷出目標熱影像的溫度隨時間上升,則進入步驟S606。於步驟S606中,判斷模組24將目標熱影像中各網格影像之溫度對時間的關係圖與預先儲存的複數個參考圖作比較,以找出與目標熱影像中各網格影像之溫度對時間的關係圖符合的參考圖。最後,進入步驟S607,於步驟S607中,判斷模組24判斷該區域內存在體溫異常的個體,於是根據目標熱影像中每個網格影像的座標,判斷模組24發出一警示訊息,以回報體溫異常之個體於該區域內所在的位置。 In step S601, the image capturing module 12 continuously generates a thermal image for an area. In step S602, the image deconstruction module 14 divides the thermal image of the region into a plurality of mesh images according to a predetermined coordinate system, wherein each of the mesh images of the thermal image has a positioning by the coordinate system. A standard. In step S603, the error rejection module 16 is used to determine a target thermal image based on the grid image exceeding a threshold temperature. In step S604, the image analysis module 22 receives the target thermal image, and generates a relationship between temperature and time of each mesh image in the target thermal image. In step S605, the transmission determination module 24 determines whether there is an individual with abnormal body temperature in the region based on the relationship map. If the determination module 24 does not determine that there is an individual whose body temperature is abnormal in the area, then the process returns to step S603 to continue to determine the target thermal image; if the determination module 24 determines that the temperature of the target thermal image increases with time, then proceeds to step S606. . In step S606, the determining module 24 compares the temperature versus time relationship of each mesh image in the target thermal image with a plurality of pre-stored reference images to find the temperature of each mesh image in the target thermal image. A reference diagram that corresponds to the time diagram. Finally, proceeding to step S607, in step S607, the determining module 24 determines that there is an individual whose body temperature is abnormal in the area, and then according to the coordinates of each grid image in the target thermal image, the determining module 24 sends a warning message to report The individual whose body temperature is abnormal is located in the area.
須說明地是,關於本實施例所提供之體溫異常個體偵測方法中的各步驟S601~S607的相關細節均已描述於前述針對圖1-圖3的相關說明中,於此便不再細述。 It should be noted that the relevant details of each step S601-S607 in the method for detecting body temperature abnormality provided by this embodiment have been described in the foregoing related descriptions of FIG. 1 to FIG. 3, and the details are no longer detailed. Said.
〔體溫異常個體偵測方法的另一實施例〕 [Another embodiment of the method for detecting abnormal body temperature]
請參照圖7,圖7為根據本發明另一例示性實施例所繪示之體溫異常個體偵測方法的流程圖。 Please refer to FIG. 7. FIG. 7 is a flowchart of a method for detecting an abnormal body temperature according to another exemplary embodiment of the present invention.
本實施例所提供之體溫異常個體偵測方法可適用於前述圖4所繪示之體溫異常個體偵測系統。復請參照圖4,前述圖4所繪示之體溫異常個體偵測系統係包括感測模組10與處理模組20。如圖4所示,感測模組10與處理模組20相連接。感測模組10主要包括影像擷取模組12、影像解構模組14與誤差排除模組16,且處理模組20主要包括影像分析模組22、判斷模組24、特徵辨識模組26、位移檢索模組27與影像還原模組28。影像解構模組14連接於影像擷取模組12,且誤差排除模組16連接於影像解構模組14。此外,影像分析模組22、特徵辨識模組26、位移檢索模組27與影像還原模組28均與判斷模組24相連接。本實施例所提供之體溫異常個體偵測方法可描述如以下之步驟,即如圖7所示。 The body temperature abnormality detecting method provided in this embodiment can be applied to the body temperature abnormality detecting system shown in FIG. 4 described above. Referring to FIG. 4 , the body temperature abnormality detecting system illustrated in FIG. 4 includes the sensing module 10 and the processing module 20 . As shown in FIG. 4, the sensing module 10 is connected to the processing module 20. The sensing module 10 mainly includes an image capturing module 12, an image deconstructing module 14 and an error eliminating module 16, and the processing module 20 mainly includes an image analyzing module 22, a determining module 24, and a feature identifying module 26, The displacement search module 27 and the image restoration module 28. The image deconstruction module 14 is connected to the image capture module 12 , and the error elimination module 16 is connected to the image deconstruction module 14 . In addition, the image analysis module 22, the feature recognition module 26, the displacement search module 27, and the image restoration module 28 are all connected to the determination module 24. The body temperature abnormality detecting method provided in this embodiment can describe the following steps, as shown in FIG. 7.
於步驟S701中,透過影像擷取模組12持續地針對一區域產生一熱影像與一實際影像。於步驟S702中,透過影像解構模組14,根據預先設定之一座標系統將該區域的熱影像與實際影像分別地劃分為複數個網格影像,其中該熱影像與該實際影像的每個網格影像均具有以該座標系統定位之一座標。於步驟S703中,透過誤差排除模組16,根據超過一門檻溫度的網格影像判斷出一目標熱影像。 In step S701, the image capturing module 12 continuously generates a thermal image and an actual image for an area. In step S702, the image deconstruction module 14 divides the thermal image and the actual image of the region into a plurality of mesh images according to a preset coordinate system, wherein the thermal image and each of the actual images are divided into a plurality of mesh images. The grid images each have a coordinate positioned by the coordinate system. In step S703, the error elimination module 16 determines a target thermal image based on the mesh image exceeding a threshold temperature.
與前述實施例所提供之體溫異常個體偵測方法的最大差異在於,於目標熱影像在步驟S703中被判斷出來後,即進入步驟S704A與步驟S704B,以對目標熱影像進行特徵辨識,進而獲得體溫異常之個體的身分資訊。 The maximum difference from the body temperature abnormality detecting method provided by the foregoing embodiment is that after the target thermal image is determined in step S703, the process proceeds to step S704A and step S704B to perform feature recognition on the target thermal image, thereby obtaining Identity information of individuals with abnormal body temperature.
於步驟S704A中,根據目標熱影像被拍攝之時間以及目標熱影像中每個網格影像的座標,影像分析模組22找出實際影像中對應的該些網格影像。接著,於步驟S704B中,影像分析模組22將該些 網格影像還原成三維影像,以由特徵辨識模組26根據該三維影像進行特徵辨識。 In step S704A, the image analysis module 22 finds the corresponding mesh images in the actual image according to the time when the target thermal image is captured and the coordinates of each of the mesh images in the target thermal image. Next, in step S704B, the image analysis module 22 performs the The mesh image is restored to a three-dimensional image, and the feature recognition module 26 performs feature recognition based on the three-dimensional image.
接下來,於步驟S705A中,根據預先設定之座標系統,位移檢索模組27紀錄目標熱影像於該區域內經過一預設時間後的位移,接著於步驟S705B中,位移檢索模組2724判斷目標熱影像於該區域內經過該預設時間後的位移是否超過一預設距離。若於步驟S705B中,位移檢索模組27判斷目標熱影像於該區域內經過該預設時間後的位移超過該預設距離,則回到步驟S703,以繼續判斷目標熱影像。另一方面,若於步驟S705B中,位移檢索模組27判斷目標熱影像於該區域內經過該預設時間後的位移未超過該預設距離,則進入步驟S706A。 Next, in step S705A, according to the coordinate system set in advance, the displacement search module 27 records the displacement of the target thermal image after a predetermined time in the region, and then in step S705B, the displacement retrieval module 2724 determines the target. Whether the displacement of the thermal image in the region after the preset time exceeds a predetermined distance. If the displacement search module 27 determines in step S705B that the displacement of the target thermal image after the preset time has elapsed in the region exceeds the preset distance, the process returns to step S703 to continue to determine the target thermal image. On the other hand, if the displacement search module 27 determines in step S705B that the displacement of the target thermal image after the predetermined time has elapsed in the region does not exceed the preset distance, the process proceeds to step S706A.
於步驟S706A中,影像分析模組22接收目標熱影像,並產生目標熱影像中各網格影像之溫度對時間的一關係圖。接著,於步驟S706B中,判斷模組24對該關係圖進行迴歸分析並獲得一溫度回歸線,並根據此溫度回歸線判斷目標熱影像的溫度是否大於等於一警戒溫度。若於步驟S706B中,判斷模組24根據此溫度回歸線判斷目標熱影像的溫度小於該警戒溫度,則回到步驟S703,以繼續判斷目標熱影像。另一方面,若於步驟S706B中,判斷模組24根據此溫度回歸線判斷目標熱影像的溫度大於等於該警戒溫度,則進入步驟S707。 In step S706A, the image analysis module 22 receives the target thermal image and generates a relationship between temperature and time of each mesh image in the target thermal image. Next, in step S706B, the determining module 24 performs regression analysis on the relationship graph and obtains a temperature regression line, and determines whether the temperature of the target thermal image is greater than or equal to a warning temperature according to the temperature regression line. If it is determined in step S706B that the temperature of the target thermal image is less than the warning temperature based on the temperature regression line, the process returns to step S703 to continue to determine the target thermal image. On the other hand, if it is determined in step S706B that the temperature of the target thermal image is greater than or equal to the alert temperature based on the temperature regression line, the process proceeds to step S707.
於步驟S707中,判斷模組24將目標熱影像中各網格影像之溫度對時間的關係圖與預先儲存的複數個參考圖作比較,以比對出與目標熱影像中各網格影像之溫度對時間的關係圖相符之一參考圖。最後,於步驟S708中,判斷模組24判斷區域內存在體溫異常的個體,並根據目標熱影像的溫度與目標熱影像中每個網格影像的座標發出一警示訊息,以回報體溫異常之個體於該區域內所在的位置以及其異常的體溫。 In step S707, the determining module 24 compares the temperature versus time relationship of each mesh image in the target thermal image with a plurality of pre-stored reference images to compare the mesh images with the target thermal image. A diagram of the temperature versus time graph is shown in the reference figure. Finally, in step S708, the determining module 24 determines that there is an individual whose body temperature is abnormal in the region, and issues a warning message according to the temperature of the target thermal image and the coordinates of each grid image in the target thermal image to report the individual whose body temperature is abnormal. The location within the area and its abnormal body temperature.
須說明地是,關於本實施例所提供之體溫異常個體偵測方法中的各步驟S701~S703、S704A~S704B、S705A~S705B、S706A~S706B、S707~S708的相關細節均已描述於前述針對圖2-4與圖5A-5B的相關說明中,於此便不再細述。 It should be noted that the relevant details of each step S701~S703, S704A~S704B, S705A~S705B, S706A~S706B, S707~S708 in the body temperature abnormality detecting method provided by the embodiment are described in the foregoing. 2-4 and 5A-5B are not described in detail herein.
〔實施例的可能功效〕 [Possible effects of the examples]
綜上所述,利用本發明所提供之體溫異常個體偵測系統與方法,便能夠自動地偵測出一區域內體溫異常的個體,並且得知該個體目前位於此區域的哪個位置,以及獲得該個體的實際影像。 In summary, the body temperature abnormality detecting system and method provided by the present invention can automatically detect an individual with abnormal body temperature in an area, and know where the individual is currently located in the area, and obtain The actual image of the individual.
也就是說,透過本發明所提供之體溫異常個體偵測系統與方法,於候診階段,醫護人員就能先了解目前位於候診間內的所有人中是否有人具有體溫異常的症狀;此外,醫護人員還能進一步得知體溫異常的人於候診間所處的位置以及此人的身份與外貌。如此一來,將有利於醫護人員對此人進行即時的照護。 That is to say, through the body temperature abnormality detecting system and method provided by the present invention, in the waiting period, the medical staff can firstly know whether any of the persons currently in the waiting room have symptoms of abnormal body temperature; in addition, the medical staff It is also possible to further know the location of the person with abnormal temperature in the waiting room and the identity and appearance of the person. In this way, it will help medical staff to take immediate care of this person.
除此之外,於本發明所提供之體溫異常個體偵測系統與方法中,透過誤差排除模組與位移檢索模組,能夠有效地排除並非體溫異常之個體的熱影像,使得本發明所提供之體溫異常個體偵測系統與方法更加精確可靠。 In addition, in the body temperature abnormality detecting system and method provided by the present invention, through the error eliminating module and the displacement searching module, the thermal image of the individual who is not abnormal in body temperature can be effectively excluded, so that the present invention provides The body temperature detection system and method for abnormal body temperature are more accurate and reliable.
以上所述僅為本發明之實施例,其並非用以侷限本發明之專利範圍。 The above description is only an embodiment of the present invention, and is not intended to limit the scope of the invention.
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW106102912A TWI626926B (en) | 2017-01-25 | 2017-01-25 | System and method for monitoring patient with abnormal body temperature |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW106102912A TWI626926B (en) | 2017-01-25 | 2017-01-25 | System and method for monitoring patient with abnormal body temperature |
Publications (2)
Publication Number | Publication Date |
---|---|
TWI626926B true TWI626926B (en) | 2018-06-21 |
TW201826999A TW201826999A (en) | 2018-08-01 |
Family
ID=63255816
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
TW106102912A TWI626926B (en) | 2017-01-25 | 2017-01-25 | System and method for monitoring patient with abnormal body temperature |
Country Status (1)
Country | Link |
---|---|
TW (1) | TWI626926B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI743379B (en) * | 2018-07-18 | 2021-10-21 | 昇雷科技股份有限公司 | Vital sign signal detecting method of the vital sign detecting system and method for evaluating a confidence of the vital sign signal |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI741502B (en) * | 2020-02-20 | 2021-10-01 | 艾科科技股份有限公司 | Time continuity detection and judgment system and method |
CN111768877A (en) * | 2020-06-16 | 2020-10-13 | 天启慧眼(北京)信息技术有限公司 | Body temperature detection method, device, equipment and computer storage medium |
TWI779367B (en) * | 2020-10-15 | 2022-10-01 | 鉭騏實業有限公司 | A compensation differential thermal imaging and testing device and method thereof |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TW200642445A (en) * | 2005-05-26 | 2006-12-01 | Sony Corp | Imaging device and method, computer program product on computer-readable medium, and imaging system |
TW201626320A (en) * | 2014-01-28 | 2016-07-16 | 姜崇義 | Intelligent monitoring system |
-
2017
- 2017-01-25 TW TW106102912A patent/TWI626926B/en active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TW200642445A (en) * | 2005-05-26 | 2006-12-01 | Sony Corp | Imaging device and method, computer program product on computer-readable medium, and imaging system |
TW201626320A (en) * | 2014-01-28 | 2016-07-16 | 姜崇義 | Intelligent monitoring system |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI743379B (en) * | 2018-07-18 | 2021-10-21 | 昇雷科技股份有限公司 | Vital sign signal detecting method of the vital sign detecting system and method for evaluating a confidence of the vital sign signal |
Also Published As
Publication number | Publication date |
---|---|
TW201826999A (en) | 2018-08-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
KR102021999B1 (en) | Apparatus for alarming thermal heat detection results obtained by monitoring heat from human using thermal scanner | |
US20210345885A1 (en) | Biological information management apparatus, biological information management method, program, and recording medium | |
TWI626926B (en) | System and method for monitoring patient with abnormal body temperature | |
JP7065350B2 (en) | System and method | |
US20200146557A1 (en) | Smart Body Temperature Screening System at Controlled Area | |
KR101729327B1 (en) | A monitoring system for body heat using the dual camera | |
JP6666488B2 (en) | Image extraction device | |
CN111344715B (en) | Object recognition system and method | |
US20170238846A1 (en) | Computer vision system for ambient long-term gait assessment | |
US11694476B2 (en) | Apparatus, system, and method of providing a facial and biometric recognition system | |
Ulleri et al. | Development of contactless employee management system with mask detection and body temperature measurement using TensorFlow | |
KR20120036638A (en) | Healthcare system integrated muti kinds sensor | |
TWI738034B (en) | Vital sign signal matching method of biological object in image and vital sign signal matching system | |
JP2017054229A (en) | Person discrimination system | |
JP2022042304A (en) | Stress estimation device, method, and program | |
TWI581760B (en) | Method for detecting abnormal heartbeat signal and electronic apparatus thereof | |
JP7090327B2 (en) | Information processing equipment, information processing method, program | |
Torres et al. | A kinect based approach to assist in the diagnosis and quantification of parkinson’s disease | |
ES2966109T3 (en) | Device and installation environment estimation program | |
KR102248251B1 (en) | Medical diagnosis support apparatus using body heat information | |
JPWO2019030880A1 (en) | Monitoring system, monitoring method, monitoring program, and recording medium thereof | |
CN112753076B (en) | Medical monitoring system | |
CN113517070A (en) | Personnel health record management system | |
WO2022118397A1 (en) | Risk value calculation device, system, method, and non-transitory computer-readable medium storing program | |
JP6061546B2 (en) | Medical information processing apparatus, medical information processing method and program |