System and method for on-road traffic density analytics using video stream mining and statistical techniques
First Claim
Patent Images
1. method for analyzing on-road traffic density comprising:
- receiving, by a traffic management computing device, a user selection of a video image capturing device from among a plurality of video image capturing devices;
receiving, by the traffic management computing device, a user selection of coordinates in one of one or more video image frames of an on-road traffic scenario captured by the selected video image capturing device such that the coordinates form a closed region of interest;
thesegmenting, by the traffic management computing device, the region of interest into one or more overlapping sub-windows;
converting, by the traffic management computing device, the one or more overlapping sub-windows into one or more feature vectors through a textural feature extraction technique;
generating, by the traffic management computing device, at least a traffic confidence value or no traffic confidence value for each of the feature vectors to classify the sub-windows as having a high traffic value or a low traffic value by a traffic density classifier;
computing, by the traffic management computing device, at least a traffic density value depending on a number of the sub-windows with a high traffic value and a total number the of sub-windows within the region of interest;
comparing, by the traffic management computing device, the traffic density value with a first set of threshold values to categorize the video image frame as having low, medium or high traffic; and
displaying, by the traffic management computing device, traffic density values at different instants in a time window to enable monitoring of a traffic trend.
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Abstract
A method and system for analyzing on-road traffic density are provided. The method involves allowing a user to select a video image capturing device and coordinates in a video image frame captured by the video image capturing device such that the coordinates form a region of interest (ROI). The ROI is processed to generate a confidence value and a traffic density value. The traffic density value is compared with a first set of threshold values. Based on the comparison, the traffic density values at different instants in a time window are displayed to enable monitoring of the traffic trend.
23 Citations
50 Claims
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1. method for analyzing on-road traffic density comprising:
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receiving, by a traffic management computing device, a user selection of a video image capturing device from among a plurality of video image capturing devices; receiving, by the traffic management computing device, a user selection of coordinates in one of one or more video image frames of an on-road traffic scenario captured by the selected video image capturing device such that the coordinates form a closed region of interest; the segmenting, by the traffic management computing device, the region of interest into one or more overlapping sub-windows; converting, by the traffic management computing device, the one or more overlapping sub-windows into one or more feature vectors through a textural feature extraction technique; generating, by the traffic management computing device, at least a traffic confidence value or no traffic confidence value for each of the feature vectors to classify the sub-windows as having a high traffic value or a low traffic value by a traffic density classifier; computing, by the traffic management computing device, at least a traffic density value depending on a number of the sub-windows with a high traffic value and a total number the of sub-windows within the region of interest; comparing, by the traffic management computing device, the traffic density value with a first set of threshold values to categorize the video image frame as having low, medium or high traffic; and displaying, by the traffic management computing device, traffic density values at different instants in a time window to enable monitoring of a traffic trend. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A method for re-training a traffic density classifier comprising:
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collecting, by a traffic management computing device, a set of misclassified video image frames captured by an image capturing device from among a plurality of image capturing devices; and utilizing, by the traffic management computing device;
a reinforcement learning to train the traffic density classifier with a valid set of video image frames corresponding to predefined settings of the image capturing device. - View Dependent Claims (22, 23)
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24. road traffic management computing device comprising:
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a processor coupled to a memory and configured to execute programmed instructions stored in the memory, comprising; receiving a user selection of a video image capturing device from among a plurality of video image capturing devices communicatively coupled to the traffic management computing device; receiving a user selection of coordinated in one of one or more video image frames of an on-road traffic scenario captured by the selected video image capturing device such that the coordinates form a closed region of interest; segmenting the region of interest into on or more overlapping sub-windows; converting the one or more overlapping sub-windows into one or more feature vectors through a textural feature extraction technique; generating at least a traffic confidence value or no traffic confidence value for each of the feature vectors to classify the sub-windows as having at least a high traffic value or a low traffic value by a traffic density classifier; computing a traffic density value depending on a number of the sub-windows with a high traffic value and a total number the sub-windows within the region of interest; comparing the traffic density value with a first set of threshold values to categorize the video image frame as having low, medium or high traffic; and displaying traffic density values at different instants in a time window to enable monitoring of a traffic end. - View Dependent Claims (25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43)
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44. A traffic management computing device comprising:
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a processor coupled to a memory and configured to execute programmed instructions stored in the memory, comprising; collecting a set of misclassified video image data of a video image capturing device from among plurality of video image capturing devices; and utilizing a reinforcement learning to train a traffic density classifier with a valid set of video image data for corresponding to predefined settings of the video image capturing devices. - View Dependent Claims (45, 46)
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47. A non-transitory computer readable medium program having stored thereon instructions for analyzing on-road traffic density comprising machine executable code which when executed by a processor, causes the processor to perform steps comprising:
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receiving a user selection of a video image capturing device from among a plurality of video image capturing devices communicatively coupled to the traffic management computing device; receiving a user selection of coordinated in one of one or more video image frames of an on-road traffic scenario captured by the selected video image capturing device such that the coordinates form a closed region of interest; segmenting the region of interest into on or more overlapping sub-windows; converting the one or more overlapping sub-windows into one or more feature vectors through a textural feature extraction technique; generating at least a traffic confidence value or no traffic confidence value for each of the feature vectors to classify the sub-windows as having at least a high traffic value or a low traffic value by a traffic density classifier; computing a traffic density value depending on a number of the sub-windows with a high traffic value and a total number the sub-windows within the region of interest; comparing the traffic density value with a first set of threshold values to categorize the video image frame as having low, medium or high traffic; and displaying traffic density values at different instants in a time window to enable monitoring of a traffic end. - View Dependent Claims (48)
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49. A non-transitory computer readable medium program having stored thereon instructions for re-training a traffic density classifier comprising machine executable code which when executed by a processor, causes the processor to perform steps comprising:
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collecting a set of misclassified video image frames captured by an image capturing device from among a plurality of image capturing devices; and utilizing a reinforcement learning to train the traffic density classifier with a valid set of video image frames corresponding to predefined settings of the image capturing device. - View Dependent Claims (50)
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Specification