WO2024045229A1 - 一种特殊场景预警屏蔽方法、装置、电子设备及存储介质 - Google Patents
一种特殊场景预警屏蔽方法、装置、电子设备及存储介质 Download PDFInfo
- Publication number
- WO2024045229A1 WO2024045229A1 PCT/CN2022/119597 CN2022119597W WO2024045229A1 WO 2024045229 A1 WO2024045229 A1 WO 2024045229A1 CN 2022119597 W CN2022119597 W CN 2022119597W WO 2024045229 A1 WO2024045229 A1 WO 2024045229A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- identified
- image
- feature points
- fire
- special scene
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000000903 blocking effect Effects 0.000 title abstract 3
- 230000009466 transformation Effects 0.000 claims abstract description 16
- 238000012423 maintenance Methods 0.000 claims abstract description 10
- 230000006870 function Effects 0.000 claims description 14
- 230000008569 process Effects 0.000 description 6
- 230000002457 bidirectional effect Effects 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 239000000779 smoke Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
Definitions
- the invention relates to the technical field of fire early warning, and specifically relates to a special scene early warning shielding method, device, electronic equipment and storage medium.
- Automatic fire alarm In the early stage of a fire, physical quantities such as smoke, heat, and light radiation generated by combustion are converted into electrical signals through fire detectors such as temperature, smoke, and light sensors, which are transmitted to the fire alarm controller and simultaneously display the occurrence of the fire. location and record the time when the fire occurred. Automatic fire alarm systems play a very important role in building fire prevention.
- the purpose of the present invention is to overcome the above technical deficiencies, provide a special scene early warning shielding method, device, electronic equipment and storage medium, to solve the problem of frequent repeated receiving in special scenes such as fire operations, fire drills, and facility maintenance in the existing technology. Technical issues with the alarm.
- the present invention provides a special scene early warning shielding method, which includes:
- a preset scale-invariant feature transformation algorithm is used to determine the matching feature points between the fire image to be identified and the special scene reference image, and the fire to be identified is determined based on the matching feature points.
- the fire image to be identified is a special scene image, and a fire warning shielding program is started.
- using a preset scale-invariant feature transformation algorithm to determine the Hausdorff distance between the fire image to be identified and the special scene reference image includes:
- the preset Gaussian differential function is used to determine the feature points to be identified in the fire image to be identified and the reference feature points of the special scene reference image, where the feature points to be identified and the reference feature points match each other. ;
- determining the feature points to be identified of the fire image to be identified and the reference feature points of the special scene reference image based on a preset Gaussian differential function includes:
- the feature points to be identified and the reference feature points that match each other are determined.
- the Gaussian differential function can be expressed by the following formula:
- ⁇ is a parameter related to size
- x and y represent the coordinates of image information pixels
- m and n are parameters related to feature points.
- determining the bidirectional Hausdorff distance between the feature point to be identified and the reference feature point includes:
- Traverse all feature points to be identified in the recognition set calculate the distance between the feature points to be identified and all feature points in the reference set, determine the corresponding first shortest distance, and construct a first of the plurality of first shortest distances. target set;
- the larger of the first one-way Hausdorff distance and the second one-way Hausdorff distance is determined to be the two-way Hausdorff distance.
- obtaining a special scene reference image includes:
- the plurality of original images are constructed into a special scene reference image set.
- the scale-invariant feature transformation algorithm determines the difference value between the fire image to be identified and the special scene reference image, and further includes determining the fire image to be identified. The difference value between each original image in the special scene reference image set.
- the present invention also provides a special scene early warning shielding device, which includes:
- the acquisition module is used to acquire fire images to be identified and special scene reference image sets
- the Hausdorff distance determination module is used to use a preset scale-invariant feature transformation algorithm to determine the matching feature points between the fire image to be identified and the special scene reference image, and determine the matching feature points based on the matching features. point, determine the Hausdorff distance between the fire image to be identified and the special scene reference image;
- a judgment module used to judge the relationship between the Hausdorff distance and the threshold
- the target module determines that the fire image to be identified is a special scene image, and starts a fire early warning shielding program.
- the present invention also provides an electronic device, including: a processor and a memory;
- the memory stores a computer-readable program that can be executed by the processor
- the present invention also provides a computer-readable storage medium that stores one or more programs, and the one or more programs can be executed by one or more processors to Implement the steps in the special scene early warning shielding method as described above.
- the special scene early warning shielding method, device, electronic equipment and storage medium provided by the present invention first determine the special scenes that do not require fire alarm early warning, and collect images of the special scenes to form special scene reference images. And obtain the fire image to be identified, and then use the preset scale-invariant feature transformation algorithm to determine the matching feature points between the fire image to be identified and the special scene reference image, and determine the fire to be identified by matching the feature points The Hausdorff distance between the image and the special scene reference image. Finally, by comparing the relationship between the Hausdorff distance and the preset threshold, the similarity between the fire image to be identified and the special scene reference image is determined. , determine whether the actual scene corresponding to the image to be identified is a special scene through the size of the similarity.
- the Hausdorff distance When the Hausdorff distance is less than the preset threshold, it indicates the similarity between the fire image to be identified and the special scene reference image.
- Maximum, that is, the actual application scenario corresponding to the fire image to be recognized may be non-fire scenarios such as fire work and equipment maintenance, and then choose to start the shielding program so that the system no longer receives alarm information in this area.
- Figure 1 is a flow chart of an embodiment of the special scene early warning shielding method provided by the present invention.
- FIG. 2 is a flow chart of an embodiment of step S102 in the special scene early warning shielding method provided by the present invention
- FIG. 3 is a flow chart of an embodiment of step S201 in the special scene early warning shielding method provided by the present invention.
- Figure 4 is a flow chart of an embodiment of step S202 in the special scene early warning shielding method provided by the present invention.
- Figure 5 is a schematic diagram of an embodiment of the special scene early warning shielding device provided by the present invention.
- FIG. 6 is a schematic diagram of the operating environment of an embodiment of the electronic device provided by the present invention.
- the invention relates to a special scene early warning shielding method, device, electronic equipment and storage medium, which can be applied to various industries and places, such as large warehouses, office buildings, shops, hotels, streets, etc., through the fire early warning system.
- the environmental conditions can be monitored in real time to keep out possible hidden dangers; currently fire detectors have different parameters for detecting fires, mainly including: temperature sensing, light sensing, smoke sensing, gas and composite detectors, and by setting fire alarms
- the system is connected with the public monitoring system to detect fires promptly and accurately and provide early warning, which can minimize losses.
- fire warnings are not required. In these situations, if too frequent fire warnings occur, it will cause fatigue and reduce the vigilance of the staff. , is not conducive to the development of fire early warning work. Therefore, it is necessary to provide a special scene early warning shielding method, device, electronic equipment and storage medium to solve the above-mentioned problems.
- the method, device, equipment or computer-readable storage medium involved in the present invention can be integrated with the above system or relatively independent.
- Figure 1 is a flow chart of a special scene early warning shielding method provided by an embodiment of the present invention. Please refer to Figure 1.
- the special scene early warning shielding method includes:
- S102 Use a preset scale-invariant feature transformation algorithm to determine the matching feature points between the fire image to be identified and the special scene reference image, and determine the to-be-identified feature points based on the matching feature points. Hausdorff distance between the fire image and the reference image of the special scene;
- special scenes that do not require fire alarm warning are first determined, images of the special scenes are collected, a special scene reference image is formed, and the fire image to be identified is obtained, and then the preset scale-invariant feature transformation is used
- the algorithm determines the matching feature points between the fire image to be identified and the special scene reference image, and determines the Hausdorff distance between the fire image to be identified and the special scene reference image through the matching feature points, and finally compares The relationship between the Hausdorff distance and the preset threshold is used to determine the similarity between the fire image to be identified and the special scene reference image. Based on the similarity, it is determined whether the actual scene corresponding to the image to be identified is a special scene.
- the Hausdorff distance when the Hausdorff distance is less than the preset threshold, it means that the similarity between the fire image to be identified and the special scene reference image is the greatest, that is, the actual application scenario corresponding to the fire image to be identified may be a fire.
- the actual application scenario corresponding to the fire image to be identified may be a fire.
- Using a preset scale-invariant feature transformation algorithm to determine matching feature points between the fire image to be identified and the special scene reference image includes:
- S201 Use the preset Gaussian differential function to determine the feature points to be identified in the fire image to be identified and the reference feature points of the special scene reference image, where the feature points to be identified and the reference feature points are in pairs. match each other;
- the SIFT algorithm i.e., scale-invariant feature transformation algorithm
- the SIFT algorithm is used to extract the feature points to be identified of the fire image to be identified and the reference feature points of the special scene reference image, where the feature points to be identified are
- the feature points on the fire image that best reflect the actual scene represented by the image, that is, the feature points to be identified can intuitively reflect the similarity between the fire image to be identified and the special scene reference image.
- the reference feature points are on the special scene reference image.
- Feature points that best reflect the actual scene represented by the image and calculate the two-way Hausdorff distance between the feature points to be identified and the reference feature points, and reflect the fire image and special scene to be identified through the Hausdorff distance
- the similarity between the reference images is used to determine the fire situation at the fire location to be identified, thereby guiding the work of the fire alarm.
- determining the feature points to be identified in the fire image to be identified and the reference feature points of the special scene reference image include:
- S302. Determine the positions of the feature points to be identified and the reference feature points, as well as the direction of the feature to be identified and the direction of the reference feature;
- S303 Construct a feature vector to be identified based on the position of the feature point to be identified and the direction of the feature to be identified;
- the feature points to be identified and the reference feature points are extracted in order to search for image positions on all scale spaces, and to identify potential scale- and rotation-invariant interest points through a preset Gaussian differential function
- the preset Gaussian differential function can be expressed by the following formula: Among them, ⁇ is a parameter related to size, x and y represent the coordinates of image information pixels, m and n are parameters related to feature points. For example, m and n can be randomly set parameter values, which are consistent with Gaussian differential function parameters. Define requirements.
- a fine-fitting model is used to determine the position and scale of the feature point to be identified and the reference feature point, and then the position is assigned to each feature point based on the local gradient direction of the image. one or more directions;
- the feature vector to be identified and the reference feature vector are constructed respectively. Finally, by comparing the feature points in the feature vector to be identified and the reference feature vector, a number of feature points to be identified that match each other are found. and reference feature points.
- determining the bidirectional Hausdorff distance between the feature point to be identified and the reference feature point includes:
- the recognition set and the reference set are respectively constructed based on the feature points to be recognized and the reference feature points that match each other in the feature vector to be recognized and the reference feature vector.
- the feature points to be recognized in the recognition set are Points that can match the reference feature points.
- the reference feature points in the reference set are points that can match the feature points to be identified.
- the process of determining the bidirectional Hausdorff distance is for the following steps:
- the longest distance between h(A, B) and h(B, A) is selected, which is the two-way Hausdorff distance of the set A and B.
- obtaining a special scene reference image further includes:
- the plurality of original images are constructed into a special scene reference image set.
- the scale-invariant feature transformation algorithm determines the difference value between the fire image to be identified and the special scene reference image, and further includes determining the fire image to be identified. The difference value between each original image in the special scene reference image set.
- the fire image to be identified and the special scene reference image set are compared for similarity until the most similar picture is found or the comparison is completed with all pictures.
- the embodiment of the present invention also provides a special scene early warning shielding device 500.
- the special scene early warning shielding device 500 includes an acquisition module 510 and a Hausdorff distance determination module 520. , judgment module 530 and target module 540.
- the acquisition module 510 is used to acquire the fire image and special scene reference image set to be identified;
- the Hausdorff distance determination module 520 is used to use a preset scale-invariant feature transformation algorithm to determine the matching feature points between the fire image to be identified and the special scene reference image, and based on the matching Feature points determine the Hausdorff distance between the fire image to be identified and the special scene reference image;
- the judgment module 530 is used to judge the relationship between the Hausdorff distance and the threshold
- Target module 540 determines that the fire image to be identified is a special scene image, and starts a fire warning shielding program.
- the present invention also provides an electronic device.
- the electronic device can be a mobile terminal, a desktop computer, a notebook, a palmtop computer, a server and other computing devices.
- the electronic device includes a processor 610, a memory 620 and a display 630.
- FIG. 6 only shows some components of the electronic device, but it should be understood that implementation of all the components shown is not required, and more or fewer components may be implemented instead.
- the memory 620 may be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device. In other embodiments, the memory 620 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), or a secure digital (Secure Digital, SD) card equipped on the electronic device. Flash Card, etc. Further, the memory 620 may also include both an internal storage unit of the electronic device and an external storage device. The memory 620 is used to store application software and various data installed on the electronic device, such as program codes for installing the electronic device. The memory 620 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a special scene early warning shielding program 640 is stored in the memory 620, and the special scene early warning shielding program 640 can be executed by the processor 610, thereby realizing the special scene early warning shielding method in each embodiment of the present application.
- a special scene early warning shielding program 640 is stored in the memory
- the processor 610 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code stored in the memory 620 or process data, such as executing special scene warnings. Shielding methods, etc.
- CPU central processing unit
- microprocessor or other data processing chip
- the display 630 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc.
- the display 630 is used to display information on the special scene early warning shielding device and to display a visual user interface.
- the components of the electronic device 610-630 communicate with each other via the system bus.
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Image Analysis (AREA)
Abstract
本发明公开了一种特殊场景预警屏蔽方法、装置、电子设备及存储介质,方法包括:获取待识别的火情图像和特殊场景参照图像;采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,并根据所述匹配特征点,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离;判断所述豪斯多夫距离与预设阈值的大小关系;若所述豪斯多夫距离小于所述预设阈值,则确定所述待识别的火情图像为特殊场景图像,并启动火灾预警屏蔽程序。本发明解决了现有技术中动火作业、消防演练、设施维保等特殊场景下频繁收到重复报警的技术问题。
Description
本发明涉及消防预警技术领域,具体涉及一种特殊场景预警屏蔽方法、装置、电子设备及存储介质。
火灾自动报警,在火灾初期,将燃烧产生的烟雾、热量和光辐射等物理量,通过感温、感烟和感光等火灾探测器变成电信号,传输到火灾报警控制器,并同时显示出火灾发生的部位,记录火灾发生的时间。火灾自动报警***在建筑火灾预防方面发挥了非常重要的作用。
随着城市消防远程监控***的广泛应用,消防远程监控***接收火灾自动报警***的报警的问题也显现出来:当出现动火作业、消防演练、定期设施维保等特殊场景时,城市消防远程监控***会短时间内频繁收到消防主机某个区域的重复报警,需要相关人员先打电话确定现场真实情况,并持续进行告警处置操作。这种重复报警处理过程中,容易造成相关人员对报警信息的麻木,可能会将其他真实警情确定为误报,引起不必要的事故及损失。
因此,结合物联网和人工智能技术,研究一种特殊场景预警屏蔽方法是非常必要的。
发明内容
本发明的目的在于克服上述技术不足,提供一种特殊场景预警屏蔽方法、装置、电子设备及存储介质,解决现有技术中动火作业、消防演练、设施维保等特殊场景下频繁收到重复报警的技术问题。
为达到上述技术目的,本发明采取了以下技术方案:
第一方面,本发明提供了一种特殊场景预警屏蔽方法,包括:
获取待识别的火情图像和特殊场景参照图像;
采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,并根据所述匹配特征点,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离;
判断所述豪斯多夫距离与预设阈值的大小关系;
若所述豪斯多夫距离小于所述预设阈值,则确定所述待识别的火情图像为特殊场景图像,并启动火灾预警屏蔽程序。
在一些实施例中,所述采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离,包括:
采用预设的高斯微分函数,确定所述待识别的火情图像待识别特征点,以及特殊场景参考图像的参考特征点,其中,所述待识别特征点和所述参考特征点两两相互匹配;
确定所述待识别特征点和所述参考特征点之间的双向豪斯多夫距离。
在一些实施例中,所述基于预设的高斯微分函数,确定所述待识别的火情图像待识别特征点,以及特殊场景参考图像的参考特征点,包括:
基于预设的高斯微分函数,提取所述待识别的火情图像的待识别特征点,以及特殊场景参考图像的参考特征点;
确定所述待识别特征点和参考特征点的位置,以及待识别特征方向和参考特征方向;
基于所述待识别特征点的位置和待识别特征方向,构建待识别特征向量;
基于所述参考特征点的位置和参考特征方向,构建参考特征向量;
根据所述待识别特征向量和参考特征向量,确定两两相互匹配的待识别特征点和所述参考特征点。
其中,σ为与尺寸相关的参数,x、y表示图像信息像素的坐标,m、n为与特征点相关的参数。
在一些实施例中,所述确定所述待识别特征点和所述参考特征点之间的双向豪斯多夫距离,包括:
构建所述待识别特征点的识别集合和参考特征点的参考集合;
遍历所述识别集合中所有待识别特征点,计算所述待识别特征点与参考集合中所有特征点的距离,确定对应的第一最短距离,并构建所述多个第一最短距离的第一目标集合;
遍历所述参考集合中所述参考特征点,计算所述参考特征点与所述识别集合中所有特征点的距离,确定对应的第二最短距离,并构建所述多个第二最短距离的第二目标集合;
根据所述第一目标集合,确定第一单向豪斯多夫距离,根据所述第二目标集合,确定第二单向豪斯多夫距离;
确定第一单向豪斯多夫距离和第二单向豪斯多夫距离中距离较大者为双向豪斯多夫距离。
在一些实施例中,所述获取特殊场景参照图像,包括:
获取多个不同场景下的原始图像,其中所述多个不同场景包括动火作业场景、消防演练和定期设施维保;
将所述多个原始图像构建成特殊场景参照图像集。
在一些实施例中,所述基于尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的差异值,还包括,确定所述待识别的火情图像与所述特书场景参照图像集中各个原始图像的差异值。
第二方面,本发明还提供了一种特殊场景预警屏蔽装置,包括:
获取模块,用于获取待识别的火情图像和特殊场景参照图像集;
豪斯多夫距离确定模块,用于采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,并根据所述匹配特征点,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离;
判断模块,用于判断所述豪斯多夫距离与阈值的大小关系;
目标模块,若所述豪斯多夫距离小于所述阈值,则确定所述待识别的火情图像为特殊场景图像,并启动火灾预警屏蔽程序。
第三方面,本发明还提供了一种电子设备,包括:处理器和存储器;
所述存储器上存储有可被所述处理器执行的计算机可读程序;
所述处理器执行所述计算机可读程序时实现如上所述的特殊场景预警屏蔽方法中的步骤。
第四方面,本发明还提供了一种计算机可读存储介质,所述计算机可读存储介质存储有一个或者多个程序,所述一个或者多个程序可被一个或者多个处理器执行,以实现如上所述的特殊场景预警屏蔽方法中的步骤。
与现有技术相比,本发明提供的特殊场景预警屏蔽方法、装置、电子设备及存储介质,首先确定无需进行火灾报警预警的特殊场景,并对特殊场景进行图像采集,形成特殊场景参照图像,并获取待识别的火情图像,随后采用预设的尺度不变特征变换算法确定待识别的火情图像和特殊场景参照图像之间匹配特征点,并通过匹配特征点,确定待识别的火情图像和特殊场景参照图像之间的豪斯多夫距离,最后通过比较豪斯多夫距离与预设阈值之间的大小关系,判定待识别的火情图像和特殊场景参照图像之间的相似性,通过相似性的大小判定待识别图像对应的实际场景是否为特殊场景,其中,当豪斯多夫距离小于预设阈值时,说明待识别的火情图像和特殊场景参照图像之间的相似性最大,即待识别的火情图像对应的实际应用场景可能为动火作业、设备维护等非火灾场景,随即选择启动屏蔽程序,以使***不再收到该区域的报警信息。
图1是本发明提供的特殊场景预警屏蔽方法的一实施例的流程图;
图2是本发明提供的特殊场景预警屏蔽方法中,步骤S102一实施例的流程图;
图3是本发明提供的特殊场景预警屏蔽方法中,步骤S201一实施例的流程图;
图4是本发明提供的特殊场景预警屏蔽方法中,步骤S202一实施例的流程图;
图5是本发明提供的特殊场景预警屏蔽装置的一实施例的示意图;
图6是本发明提供的电子设备一实施例的运行环境示意图。
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明。
本发明所涉及的一种特殊场景预警屏蔽方法、装置、电子设备及存储介质,可应用于各个行业和场所,比如大型的仓库、写字楼、商店、酒店、街道等,通过火灾预警***能够对周围的环境状况起到实时监控将可能的隐患拒之门外;目前火灾探测器探测火灾的参数不同,主要有:感温、感光、感烟、气体和复合式等探测器,并且通过将火灾报警***和公共监控***连接在一起,及时准确的发现火灾并预警,能够最大化的降低损失。
但是在一些动火作业、设备维护或消防演练等特殊性场合下,不需要进行火灾预警,在这些场合下若是出现过于频繁的火灾预警,会使工作人员产生疲惫心理,降低工作人员的警惕心,不利于火灾预警工作的开展, 因此,需要提供一种特殊场景预警屏蔽方法、装置、电子设备及存储介质,以解决上述所说的问题。本发明所涉及的方法、装置、设备或者计算机可读存储介质既可以与上述***集成在一起,也可以是相对独立的。
图1是本发明实施例提供的特殊场景预警屏蔽方法的流程图,请参阅图1,特殊场景预警屏蔽方法包括:
S101、获取待识别的火情图像和特殊场景参照图像;
S102、采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,并根据所述匹配特征点,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离;
S103、判断所述豪斯多夫距离与预设阈值的大小关系;
S104、若所述豪斯多夫距离小于所述预设阈值,则确定所述待识别的火情图像为特殊场景图像,并启动火灾预警屏蔽程序。
在本实施例中,首先确定无需进行火灾报警预警的特殊场景,并对特殊场景进行图像采集,形成特殊场景参照图像,并获取待识别的火情图像,随后采用预设的尺度不变特征变换算法确定待识别的火情图像和特殊场景参照图像之间的匹配特征点,并通过匹配的特征点确定待识别的火情图像和特殊场景参照图像之间的豪斯多夫距离,最后通过比较豪斯多夫距离与预设阈值之间的大小关系,判定待识别的火情图像和特殊场景参照图像之间的相似性,通过相似性的大小判定待识别图像对应的实际场景是否为特殊场景,其中,当豪斯多夫距离小于预设阈值时,说明待识别的火情图像和特殊场景参照图像之间的相似性最大,即待识别的火情图像对应的实际应用场景可能为动火作业、设备维护等非火灾场景,随即选择启动屏蔽程序,以使***不再收到该区域的报警信息。
在一些实施例中,请参阅图2,所述采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,包括:
S201、采用预设的高斯微分函数,确定所述待识别的火情图像待识别特征点,以及特殊场景参考图像的参考特征点,其中,所述待识别特征点和所述参考特征点两两相互匹配;
S202、确定所述待识别特征点和所述参考特征点之间的双向豪斯多夫距离。
在本实施例中,通过SIFT算法(即,尺度不变特征变换算法)提取待识别的火情图像的待识别特征点以及特殊场景参考图像的参考特征点,其中,待识别特征点为待识别火情图像上最能够体现该图像所代表的实际场景的特征点,即通过待识别特征点能够直观反映待识别火情图像与特殊场景参考图像的相似性,参考特征点为特殊场景参考图像上最能体现该图像所代表的实际场景的特征点;并计算待识别特征点和参考特征点之间的双向豪斯多夫距离,通过豪斯多夫距离反映待识别的火情图像与特殊场景参考图像之间的相似性,从而判断待识别的火情位置的火情情况,以此指导火灾报警器的工作。
在一些实施例中,请参阅图3,所述基于预设的高斯微分函数,确定所述待识别的火情图像待识别特征点,以及特殊场景参考图像的参考特征点,包括:
S301、基于预设的高斯微分函数,提取所述待识别的火情图像的待识别特征点,以及特殊场景参考图像的参考特征点;
S302、确定所述待识别特征点和参考特征点的位置,以及待识别特征方向和参考特征方向;
S303、基于所述待识别特征点的位置和待识别特征方向,构建待识别特征向量;
S304、基于所述参考特征点的位置和参考特征方向,构建参考特征向量;
S305、根据所述待识别特征向量和参考特征向量,确定两两相互匹配 的待识别特征点和所述参考特征点。
在本实施例中,提取待识别特征点和参考特征点,是为了搜索所有尺度空间上的图像位置,并且通过预设的高斯微分函数来识别潜在的具有尺度和旋转不变的兴趣点,其中,预设的高斯微分函数可由以下公式表达:
其中,σ为与尺寸相关的参数,x、y表示图像信息像素的坐标,m、n为与特征点相关的参数,如,m、n可以是随机设置的参数值,其符合高斯微分函数参数定义要求。
需要说明的是,在每个候选的位置上,通过一个拟合精细的模型来确定待识别特征点和参考特征点的位置和尺度,然后基于图像局部的梯度方向、分配给每个特征点位置一个或多个方向;
进一步的,根据特征点的位置和方向,分别构建待识别特征向量和参考特征向量,最后通过对待识别特征向量和参考特征向量中特征点进行比较,找出若干两两相互匹配的待识别特征点和参考特征点。
在一些实施例中,请参阅图4,所述确定所述待识别特征点和所述参考特征点之间的双向豪斯多夫距离,包括:
S401、构建所述待识别特征点的识别集合和参考特征点的参考集合;
S402、遍历所述识别集合中所有待识别特征点,计算所述待识别特征点与参考集合中所有特征点的距离,确定对应的第一最短距离,并构建所述多个第一最短距离的第一目标集合;
S403、遍历所述参考集合中所述参考特征点,计算所述参考特征点与所述识别集合中所有特征点的距离,确定对应的第二最短距离,并构建所述多个第二最短距离的第二目标集合;
S404、根据所述第一目标集合,确定第一单向豪斯多夫距离,根据所述第二目标集合,确定第二单向豪斯多夫距离;
S405、确定第一单向豪斯多夫距离和第二单向豪斯多夫距离中距离较 大者为双向豪斯多夫距离。
在本实施例中,识别集合和参考集合分别为基于待识别特征向量和参考特征向量中两两相互匹配的待识别特征点和参考特征点构建的,具体的,识别集中的待识别特征点为能够与参考特征点匹配的点,参考集合中的参考特征点为能够与待识别特征点匹配的点。
在一个具体的实施例中,设定识别集合为A={a1,a2,…,an},参考集合为B={b1,b2,…,bn},则确定双向豪斯多夫距离的过程为以下步骤:
首先取A集合中的一点a1,计算a1到B集合中所有点的距离,保留最短的距离d1;
随后遍历A集合中所有点,计算出d2,…,dn;
随后比较所有的距离{d1,d2,…,dn},选出最长的距离dx,即为A→B的单向豪斯多夫距离,记为h(A,B);
随后按照上述步骤,计算B→A的单向豪斯多夫距离,记为h(B,A);
最后选出h(A,B)和h(B,A)中最长的距离,即为A,B集合的双向豪斯多夫距离。
在一些实施例中,所述获取特殊场景参照图像,还包括:
获取多个不同场景下的原始图像,其中所述多个不同场景包括动火作业场景、消防演练和定期设施维保;
将所述多个原始图像构建成特殊场景参照图像集。
在本实施例中,针对不同的特殊场景,包括但限于动火作业、设备维护或消防演练,均需采集相应的样本图像形成特殊场景参照图像集,以避免在实际判别过程中出现漏选的情况。
在一些实施例中,所述基于尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的差异值,还包括,确定所述待识别的火情图像与所述特殊场景参照图像集中各个原始图像的差异值。
在本实施例中,将待识别火情图像和特殊场景参照图像集中的每一张 图片进行相似性对比,直至找到最相似的图片或与所有的图片完成对比为止。
基于上述特殊场景预警屏蔽方法,本发明实施例还相应的提供一种特殊场景预警屏蔽装置500,请参阅图5,该特殊场景预警屏蔽装置500包括获取模块510、豪斯多夫距离确定模块520、判断模块530和目标模块540。
获取模块510,用于获取待识别的火情图像和特殊场景参照图像集;
豪斯多夫距离确定模块520,用于采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,并根据所述匹配特征点,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离;
判断模块530,用于判断所述豪斯多夫距离与阈值的大小关系;
目标模块540,若所述豪斯多夫距离小于所述阈值,则确定所述待识别的火情图像为特殊场景图像,并启动火灾预警屏蔽程序。
如图6所示,基于上述特殊场景预警屏蔽方法,本发明还相应提供了一种电子设备,该电子设备可以是移动终端、桌上型计算机、笔记本、掌上电脑及服务器等计算设备。该电子设备包括处理器610、存储器620及显示器630。图6仅示出了电子设备的部分组件,但是应理解的是,并不要求实施所有示出的组件,可以替代的实施更多或者更少的组件。
存储器620在一些实施例中可以是该电子设备的内部存储单元,例如电子设备的硬盘或内存。存储器620在另一些实施例中也可以是电子设备的外部存储设备,例如电子设备上配备的插接式硬盘,智能存储卡(Smart Media Card,SMC),安全数字(Secure Digital,SD)卡,闪存卡(Flash Card)等。进一步地,存储器620还可以既包括电子设备的内部存储单元也包括外部存储设备。存储器620用于存储安装于电子设备的应用软件及各类数据,例如安装电子设备的程序代码等。存储器620还可以用于暂时地存储已经输出或者将要输出的数据。在一实施例中,存储器620上存储 有特殊场景预警屏蔽程序640,该特殊场景预警屏蔽程序640可被处理器610所执行,从而实现本申请各实施例的特殊场景预警屏蔽方法。
处理器610在一些实施例中可以是一中央处理器(Central Processing Unit,CPU),微处理器或其他数据处理芯片,用于运行存储器620中存储的程序代码或处理数据,例如执行特殊场景预警屏蔽方法等。
显示器630在一些实施例中可以是LED显示器、液晶显示器、触控式液晶显示器以及OLED(Organic Light-Emitting Diode,有机发光二极管)触摸器等。显示器630用于显示在所述特殊场景预警屏蔽设备的信息以及用于显示可视化的用户界面。电子设备的部件610-630通过***总线相互通信。
当然,本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程,是可以通过计算机程序来指令相关硬件(如处理器,控制器等)来完成,所述的程序可存储于一计算机可读取的存储介质中,该程序在执行时可包括如上述各方法实施例的流程。其中所述的存储介质可为存储器、磁碟、光盘等。
以上所述本发明的具体实施方式,并不构成对本发明保护范围的限定。任何根据本发明的技术构思所做出的各种其他相应的改变与变形,均应包含在本发明权利要求的保护范围内。
Claims (10)
- 一种特殊场景预警屏蔽方法,其特征在于,包括:获取待识别的火情图像和特殊场景参照图像;采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,并根据所述匹配特征点,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离;判断所述豪斯多夫距离与预设阈值的大小关系;若所述豪斯多夫距离小于所述预设阈值,则确定所述待识别的火情图像为特殊场景图像,并启动火灾预警屏蔽程序。
- 根据权利要求1所述的特殊场景预警屏蔽方法,其特征在于,所述采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,包括:采用预设的高斯微分函数,确定所述待识别的火情图像待识别特征点,以及特殊场景参考图像的参考特征点,其中,所述待识别特征点和所述参考特征点两两相互匹配。
- 根据权利要求2所述的特殊场景预警屏蔽方法,其特征在于,所述采用预设的高斯微分函数,确定所述待识别的火情图像待识别特征点,以及特殊场景参考图像的参考特征点,包括:基于预设的高斯微分函数,提取所述待识别的火情图像的待识别特征点,以及特殊场景参考图像的参考特征点;确定所述待识别特征点和参考特征点的位置,以及待识别特征方向和参考特征方向;基于所述待识别特征点的位置和待识别特征方向,构建待识别特征向量;基于所述参考特征点的位置和参考特征方向,构建参考特征向量;根据所述待识别特征向量和参考特征向量,确定两两相互匹配的待识别特征点和所述参考特征点。
- 根据权利要求2所述的特殊场景预警屏蔽方法,其特征在于,所述并根据所述匹配特征点,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离,包括:构建所述待识别特征点的识别集合和参考特征点的参考集合;遍历所述识别集合中所有待识别特征点,计算所述待识别特征点与参考集合中所有特征点的距离,确定对应的第一最短距离,并构建所述多个第一最短距离的第一目标集合;遍历所述参考集合中所述参考特征点,计算所述参考特征点与所述识别集合中所有特征点的距离,确定对应的第二最短距离,并构建所述多个第二最短距离的第二目标集合;根据所述第一目标集合,确定第一单向豪斯多夫距离,根据所述第二目标集合,确定第二单向豪斯多夫距离;确定第一单向豪斯多夫距离和第二单向豪斯多夫距离中距离较大者为双向豪斯多夫距离。
- 根据权利要求1所述的特殊场景预警屏蔽方法,其特征在于,所述获取特殊场景参照图像,还包括:获取多个不同场景下的原始图像,其中所述多个不同场景包括动火作业场景、消防演练和定期设施维保;将所述多个原始图像构建成特殊场景参照图像集。
- 根据权利要求6所述的特殊场景预警屏蔽方法,其特征在于,所述基于尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景 参照图像之间的差异值,还包括,确定所述待识别的火情图像与所述特殊场景参照图像集中各个原始图像的差异值。
- 一种特殊场景预警屏蔽装置,其特征在于,包括:获取模块,用于获取待识别的火情图像和特殊场景参照图像集;豪斯多夫距离确定模块,用于采用预设的尺度不变特征变换算法,确定所述待识别的火情图像和所述特殊场景参照图像之间的匹配特征点,并根据所述匹配特征点,确定所述待识别的火情图像和所述特殊场景参照图像之间的豪斯多夫距离;判断模块,用于判断所述豪斯多夫距离与阈值的大小关系;目标模块,若所述豪斯多夫距离小于所述阈值,则确定所述待识别的火情图像为特殊场景图像,并启动火灾预警屏蔽程序。
- 一种电子设备,其特征在于,包括:处理器和存储器;所述存储器上存储有可被所述处理器执行的计算机可读程序;所述处理器执行所述计算机可读程序时实现如上述所述的特殊场景预警屏蔽方法中的步骤。
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质存储有一个或者多个程序,所述一个或者多个程序可被一个或者多个处理器执行,以实现如上述所述的特殊场景预警屏蔽方法中的步骤。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211039365.6 | 2022-08-29 | ||
CN202211039365.6A CN115457387A (zh) | 2022-08-29 | 2022-08-29 | 一种特殊场景预警屏蔽方法、装置、电子设备及存储介质 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2024045229A1 true WO2024045229A1 (zh) | 2024-03-07 |
Family
ID=84301112
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2022/119597 WO2024045229A1 (zh) | 2022-08-29 | 2022-09-19 | 一种特殊场景预警屏蔽方法、装置、电子设备及存储介质 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN115457387A (zh) |
WO (1) | WO2024045229A1 (zh) |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103238159A (zh) * | 2010-09-28 | 2013-08-07 | 华为技术有限公司 | 用于图像认证的***和方法 |
US20200211221A1 (en) * | 2018-12-26 | 2020-07-02 | Hitachi, Ltd. | Object recognition device and object recognition method |
CN112446431A (zh) * | 2020-11-27 | 2021-03-05 | 鹏城实验室 | 特征点提取与匹配方法、网络、设备及计算机存储介质 |
CN113486942A (zh) * | 2021-06-30 | 2021-10-08 | 武汉理工光科股份有限公司 | 一种重复火警判定方法、装置、电子设备及存储介质 |
US20210319250A1 (en) * | 2020-04-09 | 2021-10-14 | Sensetime International Pte. Ltd. | Matching method and apparatus, electronic device, computer-readable storage medium, and computer program |
CN114119645A (zh) * | 2021-11-25 | 2022-03-01 | 推想医疗科技股份有限公司 | 一种图像分割质量的确定方法、***、设备及介质 |
-
2022
- 2022-08-29 CN CN202211039365.6A patent/CN115457387A/zh active Pending
- 2022-09-19 WO PCT/CN2022/119597 patent/WO2024045229A1/zh unknown
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103238159A (zh) * | 2010-09-28 | 2013-08-07 | 华为技术有限公司 | 用于图像认证的***和方法 |
US20200211221A1 (en) * | 2018-12-26 | 2020-07-02 | Hitachi, Ltd. | Object recognition device and object recognition method |
US20210319250A1 (en) * | 2020-04-09 | 2021-10-14 | Sensetime International Pte. Ltd. | Matching method and apparatus, electronic device, computer-readable storage medium, and computer program |
CN112446431A (zh) * | 2020-11-27 | 2021-03-05 | 鹏城实验室 | 特征点提取与匹配方法、网络、设备及计算机存储介质 |
CN113486942A (zh) * | 2021-06-30 | 2021-10-08 | 武汉理工光科股份有限公司 | 一种重复火警判定方法、装置、电子设备及存储介质 |
CN114119645A (zh) * | 2021-11-25 | 2022-03-01 | 推想医疗科技股份有限公司 | 一种图像分割质量的确定方法、***、设备及介质 |
Non-Patent Citations (1)
Title |
---|
XIAOHAO: "The most Detailed Implementation of SIFT Algorithm Principle in the Entire Network", HUAWEI CLOUD, 24 March 2022 (2022-03-24), XP093144950, Retrieved from the Internet <URL:https://huaweicloud.csdn.net/63802f3edacf622b8df8643b.html> [retrieved on 20240325] * |
Also Published As
Publication number | Publication date |
---|---|
CN115457387A (zh) | 2022-12-09 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10839197B2 (en) | Monitoring system, monitoring camera, and management device | |
KR102128533B1 (ko) | 머신러닝을 이용한 상품 인식을 위한 특징점 관리 장치 및 방법 | |
JP6487944B2 (ja) | 自然言語画像検索 | |
Ko et al. | Modeling and formalization of fuzzy finite automata for detection of irregular fire flames | |
JP2022002399A (ja) | 監視システム及び監視方法 | |
Godil et al. | Performance metrics for evaluating object and human detection and tracking systems | |
US20160350583A1 (en) | Image search system and image search method | |
WO2022227764A1 (zh) | 事件检测的方法、装置、电子设备以及可读存储介质 | |
JP2015153195A (ja) | ジェスチャ認識装置およびジェスチャ認識装置の制御方法 | |
US20150078618A1 (en) | System for tracking dangerous situation in cooperation with mobile device and method thereof | |
CN112861673A (zh) | 一种用于监控视频多目标检测的去误报预警方法和*** | |
JP2021534480A (ja) | 顔認識方法、装置、電子機器及びコンピュータ不揮発性読み取り可能な記憶媒体 | |
US20240201835A1 (en) | Information processing apparatus, information processing method, and storage medium for person search | |
Mei et al. | Human intrusion detection in static hazardous areas at construction sites: Deep learning–based method | |
Yang et al. | Automatic detection of falling hazard from surveillance videos based on computer vision and building information modeling | |
US8552862B2 (en) | System and method for detecting multi-level intrusion events and computer program product thereof | |
WO2024045229A1 (zh) | 一种特殊场景预警屏蔽方法、装置、电子设备及存储介质 | |
CN113314230A (zh) | 基于大数据的智能防疫方法、装置、设备及存储介质 | |
US10783365B2 (en) | Image processing device and image processing system | |
CN115810132B (zh) | 一种裂缝朝向识别方法、装置、设备及存储介质 | |
CN114864106A (zh) | 一种公交车疫情监测***及方法 | |
US20230052278A1 (en) | Information processing apparatus, information processing method, and storage medium | |
Barlybayev et al. | Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study | |
CN113570622A (zh) | 一种障碍物确定方法、装置、电子设备以及存储介质 | |
JP6218102B2 (ja) | 情報処理システム、情報処理方法及びプログラム |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 22957048 Country of ref document: EP Kind code of ref document: A1 |