AU2021107425A4 - Obstacle detection system in hybrid classification of heterogeneous environment using data mining techniques and method thereof - Google Patents
Obstacle detection system in hybrid classification of heterogeneous environment using data mining techniques and method thereof Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 76
- 238000001514 detection method Methods 0.000 title claims abstract description 34
- 238000007418 data mining Methods 0.000 title claims abstract description 15
- 238000000605 extraction Methods 0.000 claims abstract description 25
- 238000012360 testing method Methods 0.000 claims abstract description 14
- 230000003287 optical effect Effects 0.000 claims abstract description 11
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 7
- 230000003111 delayed effect Effects 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 2
- 230000008901 benefit Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000004075 alteration Effects 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000001788 irregular Effects 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
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Abstract
The present invention generally relates to an obstacle detection
system in hybrid classification of heterogeneous environment based on
data mining techniques comprises an optical sensor configured for
capturing real time image/video of the road; a pre-processing module in
continuation with the optical sensor for extracting region of interest from
the captured image/video; a feature extraction module for extracting
image features for producing a global feature representation (image
feature vector); and a classifier trained by the global feature
representation for producing a typical way of the outcome of exercise
procedure that remains then used through the testing component to
forecast the consequence aimed at the new observed imageries thereafter
categorizes the obstacle founded taking place category.
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Description
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The present disclosure relates to rush-hour traffic monitoring and detection of the automobile as an obstacle using ODMS. More particularly, the present invention relates to an obstacle detection system and method in hybrid classification of heterogeneous environment based on data mining techniques.
Transportation is concerned with locomotion of goods and human beings. In bust cities people may face regular traffic jams due to availability of high number of vehicle or by breaking traffic rules by driving the vehicles in a wrong direction or in a wrong side or by wrong parking, and the like.
However, transportation can be used for good as well as for bad. The smugglers, robbers and murderers use transportation before or after the crime and cannot be traced easily. There is no system available that can trace the vehicles or smugglers, robbers and murderers sitting inside the vehicle, which results in delay or no detection of criminal in some cases.
However, if there will be any system available that can identify the irregular vehicles or persons doing irregular things inside the vehicle may help in stopping the crime or tracing the person after crime.
In the view of the forgoing discussion, it is clearly portrayed that there is a need to have an obstacle detection system and method in hybrid classification of heterogeneous environment based on data mining techniques.
The present disclosure seeks to provide an obstacle detection system and method to deliver remarkable value concerning obstacle surveillance besides identification created on an obstacle as automobile appearance as an alternative of the automobile attached license plate.
In an embodiment, an obstacle detection system in hybrid classification of heterogeneous environment based on data mining techniques is disclosed. The system includes an optical sensor configured for capturing real time image/video of the road. The system further includes a pre-processing module in continuation with the optical sensor for extracting region of interest from the captured image/video. The system further includes a feature extraction module for extracting image features for producing a global feature representation (image feature vector). The system further includes a classifier trained by the global feature representation for producing a typical way of the outcome of exercise procedure that remains then used through the testing component to forecast the consequence aimed at the new observed imageries thereafter categorizes the obstacle founded taking place category.
In an embodiment, section of concentration extraction besides feature taking out remain shared aimed at together exercise besides testing responsibilities.
In an embodiment, feature extraction component computes discriminatory features beginning the mined area of consideration.
In an embodiment, universal feature depiction part produces a typical encrypting of picture features reliant scheduled the practical method, wherein universal feature depiction component custom the complete structures usual of a picture aimed at a uniform depiction.
In an embodiment, a non-necessitate explicit universal feature of depiction method is sued herein for regularly measuring area of attention extraction by way of a portion of automobile recognition procedure.
In an embodiment, extraction methods are feature transform, robust feature, histogram of focused happening gradient, and complete depiction of three-dimensional envelope.
In an embodiment, the histogram of focused happening gradient besides a complete depiction of three-dimensional envelope does not rely on interest points, whereas histogram of focused happening gradient splits the image interested in grids besides calculates a histogram aimed at separately part of grid however complete depiction of three dimensional envelope stands calculated aimed at the complete image.
In an embodiment, the classifier is trained to identify the obstacle as automobile model under the concert is delayed through the precision of the automobile cover beside the assessed bumper toward street surface elevation.
In an embodiment, extraction of information begins from inputs as depiction features later obstacle through a method of automobile imageries besides generating several dissimilar feature directions towards denote the dataset by employing two prevailing classification procedures, wherein two prevailing classification procedures are arbitrary forest besides provision route machine.
In another embodiment, an obstacle detection method in hybrid classification of heterogeneous environment based on data mining techniques is disclosed. The method includes capturing real time image/video of the road. The method further includes extracting region of interest from the captured image/video thereby confirming detected obstacle as car. The method further includes extracting image features for producing a global feature representation (image feature vector). The method further includes producing a typical way of the outcome of exercise procedure that remains then used through the testing component to forecast the consequence aimed at the new observed imageries thereafter categorizes the obstacle founded taking place category.
An object of the present disclosure is to deliver remarkable value concerning obstacle surveillance besides identification created on an obstacle as automobile appearance as an alternative of the automobile attached license plate.
Another object of the present disclosure is to promote smart transport scheme applications, used in programmed automobile investigation, rush-hour traffic management, motorist support schemes, road traffic performance examination, besides road traffic nursing, and many more.
Yet another object of the present invention is to deliver an expeditious and cost-effective method for rush-hour traffic monitoring and detection of the automobile as an obstacle.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a block diagram of an obstacle detection system in hybrid classification of heterogeneous environment based on data mining techniques in accordance with an embodiment of the present disclosure; Figure 2 illustrates a circuit block diagram of an obstacle detection system in hybrid classification of heterogeneous environment based on data mining techniques in accordance with an embodiment of the present disclosure; Figure 3 illustrates a flow chart of an obstacle detection method in hybrid classification of heterogeneous environment based on data mining techniques in accordance with an embodiment of the present disclosure; and Figure 4 illustrates an architecture of obstacle detection model System in accordance with an embodiment of the present disclosure.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
Referring to Figure 1, a block diagram of an obstacle detection system in hybrid classification of heterogeneous environment based on data mining techniques is illustrated in accordance with an embodiment of the present disclosure. The system 100 includes an optical sensor 102 configured for capturing real time image/video of the road. The optical sensor 102 is preferably a camera 102. A plurality of cameras 102 can be employed at multiple places for capturing wide area as per requirement of monitoring.
In an embodiment, a pre-processing module 104 is in continuation with the optical sensor 102 for extracting region of interest from the captured image/video. The pre-processing module 104 is engaged with a central processing unit 110 for removing noise from the images.
In an embodiment, feature extraction module 106 is used herein for extracting image features for producing a global feature representation (image feature vector). The image features are edge, corner, points and the like.
In an embodiment, a classifier 108 is trained by the global feature representation for producing a typical way of the outcome of exercise procedure that remains then used through the testing component to forecast the consequence aimed at the new observed imageries thereafter categorizes the obstacle founded taking place category. The classifier 108 is equipped with a cloud storage 114 for storing the training images and identified images.
In an embodiment, section of concentration extraction besides feature taking out remain shared aimed at together exercise besides testing responsibilities.
In an embodiment, feature extraction component computes discriminatory features beginning the mined area of consideration.
In an embodiment, universal feature depiction part produces a typical encrypting of picture features reliant scheduled the practical method, wherein universal feature depiction component custom the complete structures usual of a picture aimed at a uniform depiction.
In an embodiment, a non-necessitate explicit universal feature of depiction method is sued herein for regularly measuring area of attention extraction by way of a portion of automobile recognition procedure.
In an embodiment, extraction methods are feature transform, robust feature, histogram of focused happening gradient, and complete depiction of three-dimensional envelope.
In an embodiment, the histogram of focused happening gradient besides a complete depiction of three-dimensional envelope does not rely on interest points, whereas histogram of focused happening gradient splits the image interested in grids besides calculates a histogram aimed at separately part of grid however complete depiction of three dimensional envelope stands calculated aimed at the complete image.
In an embodiment, the classifier 108 is trained to identify the obstacle as automobile model under the concert is delayed through the precision of the automobile cover beside the assessed bumper toward street surface elevation.
In an embodiment, extraction of information begins from inputs as depiction features later obstacle through a method of automobile imageries besides generating several dissimilar feature directions towards denote the dataset by employing two prevailing classification procedures, wherein two prevailing classification procedures are arbitrary forest besides provision route machine.
Figure 2 illustrates a circuit block diagram of an obstacle detection system in hybrid classification of heterogeneous environment based on data mining techniques in accordance with an embodiment of the present disclosure. The camera 102 is employed at multiple places for capturing real time image/video of the road in a user defined area. The central processing unit 110 is connected with the camera 102 for receiving the captured image/video and performing pre-processing, feature extraction and classification of the objects from the captured image/video. A communication module 112 is connected with the central processing unit
110 for wirelessly connecting the central processing unit 110 with the cloud storage 114. The cloud storage 114 is used to store the training images and the identified object images and identification related information. A display unit 116 is connected with the central processing unit 110 for displaying the identified object along with the information related to the identified object. The display unit 116 can be a screen or a user device such as mobile, laptop, PC and the like for performing remote monitoring of the object.
Figure 3 illustrates a flow chart of an obstacle detection method in hybrid classification of heterogeneous environment based on data mining techniques in accordance with an embodiment of the present disclosure. At step 302, the method 300 includes capturing real time image/video of the road by deploying a plurality of optical sensors 102.
At step 304, the method 300 includes extracting region of interest from the captured image/video thereby confirming detected obstacle as car.
At step 306, the method 300 includes extracting image features for producing a global feature representation (image feature vector).
At step 308, the method 300 includes producing a typical way of the outcome of exercise procedure that remains then used through the testing component to forecast the consequence aimed at the new observed imageries thereafter categorizes the obstacle founded taking place category.
Figure 4 illustrates an architecture of obstacle detection model System in accordance with an embodiment of the present disclosure. The primary task in obstacle detection model system remains to substantiate the presence of automobiles as difficulty in the contribution image before video frame. In the popular circumstance of nonappearance of the automobile, an additional dispensation remains not at all compulsory. Though, the input image otherwise movie frame remains treated additionally towards distinguishing the automobile in a situation of the presence of the automobile obstacle.
When the presence of an automobile remains deep-rooted, the subsequent attempt to eliminate the background besides the unwanted portion of the picture separates the automobile within the picture.
Area of concentration towards signify the portion of the automobile popular the picture that delivers the discriminatory besides projecting features. The discriminative besides prominent structures remain different amongst dissimilar automobiles. The forward automobile images in our exertion towards a strategy of "Obstacle Detection Model System" besides cast-off bumper, front lights, besides bonnet extent as a region of interest red bounding. The province of concentration extraction component eliminates the contextual as well as a portion of the automobile as of slightly certain image that can be helpful during classification and can degrade the classification performance.
The following stage remains towards extract image features besides producing a global feature representation (image feature vector) that will be used by the classifier 108 to train the system and predict the new upcoming images. Section of Concentration Extraction besides feature taking out remain shared aimed at together exercise besides testing responsibilities.
Feature mining component computes the discriminatory features beginning the mined Area of Consideration. The universal feature depiction part produces a typical encrypting of picture features reliant scheduled the practical method. Universal feature depiction component custom the complete structures usual of a picture aimed at a uniform depiction, we may use the non-necessitate explicit universal feature of depiction method. Area of attention extraction remains regularly measured by way of a portion of automobile recognition procedure.
The classifier component produces a typical way of the outcome of exercise procedure that remains then used through the testing component to forecast the consequence aimed at the anew observed imageries. Henceforth, the arrows from the exercise procedure to the challenging procedure signify the practice of the representations of trendy challenging procedures shaped through the exercise procedure. Obstacle Detection Model System categorizes the obstacle founded taking place category. Systems progress the ODMS performance concerning acknowledgment degree in addition processing speed by way of related to existing structures.
Mining Methods used:
1. Feature transform that is Scale Invariant
2. Robust feature that speeds things up
3. Histogram of focused happening gradient
4. Complete depiction of three-dimensional envelope
The projecting attention opinions remain noticed trendy circumstance of feature transform that is Scale Invariant and Robust feature that speeds things up besides individually interest point signified through a feature descriptor. The Histogram of focused happening Gradient besides a complete depiction of Three-dimensional Envelope does not rely on interest points, histogram of Focused happening Gradient splits the image interested in grids besides calculates a histogram aimed at separately part of grid however Complete Depiction of Three dimensional Envelope stands calculated aimed at the complete image.
Classification Outlines
(1) Obstacle Detection Model System is only multi class Sustenance Route Machine
(2) Obstacle Detection Model System is collaborative of multi-class Sustenance Route Machine
(3) Obstacle Detection Model works on System Random Forest
(4) Obstacle Detection Model System Separation overcome the Sustenance Route Machine
Obstacle detection model system identifies the obstacle as automobile model under the concert is delayed through the precision of the automobile cover beside the assessed bumper toward street surface elevation. ODMS techniques had covered separation of overlapped automobiles identify the models of automobile accurately. ODMS detect vehicles accurately in traffic jams and all-weather conditions. Obstacle detection model system is the stage and carries significant characteristics in smart transportation systems. The outstanding towards the alteration amongst dissimilar obstacles as automobile representations acknowledgment datasets, the precision is fewer. ODMS gives better performance under high speed of automobile under heterogeneous environment.
An Obstacle Detection Model System (ODMS) can deliver remarkable value concerning Obstacle Surveillance besides identification created on an Obstacle as automobile appearance as an alternative of the automobile attached license plate. An Obstacle Detection Model System is an important component of many Smart Transport Scheme applications, used in programmed automobile investigation, rush-hour traffic management, motorist support schemes, road traffic performance examination, besides road traffic nursing, and many more. The Obstacle
Detection Model System can decrease the cost for such applications with better accuracy and performance under heterogeneous environments. An exclusive besides vigorous Obstacle Detection Model System which is capable to grip under tests of the heterogeneous situation through short illumination disorder besides distinguishing automobiles through high precision.
Extraction of information begins from inputs as depiction features later obstacle through a method of automobile imageries besides generating several dissimilar feature directions towards denote the dataset. Thus, two prevailing classification procedures, Arbitrary Forest besides Provision Route Machine, popularize this work. The automobiles' imageries in the dataset reproduce real-world circumstances such by way of dissimilar meteorological conditions, dissimilar illumination experience, and obstructed descriptions besides dissimilar observing viewpoints. The proposed timeless Multiclass Classification Technique explained over fitting delinquent. As soon as representations accomplished on an insignificant dataset remain additional possible towards understanding forms that ensure not occur, which outcomes popular high change besides very high mistake happening an examination set. Therefore, the precision grows the reduction by convinced situation wherever the facts obligated with training illustration.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed.
Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
Claims (10)
1. An obstacle detection system in hybrid classification of heterogeneous environment based on data mining techniques, the system comprises:
an optical sensor configured for capturing real time image/video of the road; a pre-processing module in continuation with the optical sensor for extracting region of interest from the captured image/video; a feature extraction module for extracting image features for producing a global feature representation (image feature vector); and a classifier trained by the global feature representation for producing a typical way of the outcome of exercise procedure that remains then used through the testing component to forecast the consequence aimed at the new observed imageries thereafter categorizes the obstacle founded taking place category.
2. The system as claimed in claim 1, wherein section of concentration extraction besides feature taking out remain shared aimed at together exercise besides testing responsibilities.
3. The system as claimed in claim 1, wherein feature extraction component computes discriminatory features beginning the mined area of consideration.
4. The system as claimed in claim 1, wherein universal feature depiction part produces a typical encrypting of picture features reliant scheduled the practical method, wherein universal feature depiction component custom the complete structures usual of a picture aimed at a uniform depiction.
5. The system as claimed in claim 4, wherein a non-necessitate explicit universal feature of depiction method is sued herein for regularly measuring area of attention extraction by way of a portion of automobile recognition procedure.
6. The system as claimed in claim 1, wherein extraction methods are feature transform, robust feature, histogram of focused happening gradient, and complete depiction of three-dimensional envelope.
7. The system as claimed in claim 6, wherein the histogram of focused happening gradient besides a complete depiction of three-dimensional envelope does not rely on interest points, whereas histogram of focused happening gradient splits the image interested in grids besides calculates a histogram aimed at separately part of grid however complete depiction of three-dimensional envelope stands calculated aimed at the complete image.
8. The system as claimed in claim 1, wherein the classifier is trained to identify the obstacle as automobile model under the concert is delayed through the precision of the automobile cover beside the assessed bumper toward street surface elevation.
9. The system as claimed in claim 1, wherein extraction of information begins from inputs as depiction features later obstacle through a method of automobile imageries besides generating several dissimilar feature directions towards denote the dataset by employing two prevailing classification procedures, wherein two prevailing classification procedures are arbitrary forest besides provision route machine.
10. An obstacle detection method in hybrid classification of heterogeneous environment based on data mining techniques, the method comprises: capturing real time image/video of the road; extracting region of interest from the captured image/video thereby confirming detected obstacle as car; extracting image features for producing a global feature representation (image feature vector); and producing a typical way of the outcome of exercise procedure that remains then used through the testing component to forecast the consequence aimed at the new observed imageries thereafter categorizes the obstacle founded taking place category.
EDITORIAL NOTE Aug 2021
2021107425
There are 4 pages of drawings only.
An Optical Sensor A Pre-processing 102 Module 104
A Feature Extraction A Classifier 108 Module 106
Figure 1
Central Camera 102 Processing Unit Display Unit 116 110
Communication Module 112
Cloud Storage capturing real time image/video of the road 302 extracting region of interest from the captured image/video thereby confirming detected obstacle 304 as car extracting image features for producing a global feature representation (image feature vector) 306 producing a typical way of the outcome of exercise procedure that remains then used through the 308 testing component to forecast the consequence aimed at the new observed imageries thereafter categorizes the obstacle founded taking place category
Figure 3
Figure 4
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