SYSTEM AND METHOD FOR ON-ROAD TRAFFIC DENSITY ANALYTICS USING VIDEO STREAM MINING AND STATISTICAL TECHNIQUES
First Claim
1. A computer-implemented method for analyzing on-road traffic density comprising:
- a. allowing a user to select a video image capturing device from among a plurality of video image capturing devices;
b. allowing the user to select coordinates in one of the 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 (ROI);
c. processing the ROI, wherein the processing comprises;
i. segmenting the ROI into one or more overlapping sub-windows,ii. converting the one or more overlapping sub-windows into one or more feature vectors through a textural feature extraction technique;
d. generating a traffic confidence value or no traffic confidence value for each of the feature vectors by a traffic density classifier;
e. computing a traffic density value depending on a number of sub-windows with high traffic and total number of sub-windows within the ROI;
f. 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
g. displaying 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.
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Citations
50 Claims
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1. A computer-implemented method for analyzing on-road traffic density comprising:
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a. allowing a user to select a video image capturing device from among a plurality of video image capturing devices; b. allowing the user to select coordinates in one of the 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 (ROI); c. processing the ROI, wherein the processing comprises; i. segmenting the ROI into one or more overlapping sub-windows, ii. converting the one or more overlapping sub-windows into one or more feature vectors through a textural feature extraction technique; d. generating a traffic confidence value or no traffic confidence value for each of the feature vectors by a traffic density classifier; e. computing a traffic density value depending on a number of sub-windows with high traffic and total number of sub-windows within the ROI; f. 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 g. displaying 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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a. collecting a set of misclassified video image frames captured by an image capturing device from among a plurality of image capturing devices; and b. 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 (22, 23)
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24. A system for analyzing on-road traffic density, the system comprising:
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a. a plurality of video image capturing devices configured to capture one or more video image frames of an on-road traffic scenario; b. a user interface device configured to; i. select a video image capturing device from among the plurality of video image capturing devices; ii. select coordinates in one of the one or more video image frames captured by the selected video image capturing device such that the coordinates form a closed region of interest (ROI). c. a processing engine configured to; i. segment the ROI into one or more overlapping sub-windows; and ii. convert the one or more overlapping sub-windows into one or more feature vectors through a textural feature extraction technique; d. a traffic density classification engine configured to; i. generate a traffic classification confidence value or a no-traffic classification confidence value for each of the feature vectors; ii. compute a traffic density value based on a number of sub-windows with high traffic and total number of sub-windows within the ROI; and iii. compare 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 e. a display unit configured to display traffic density values at different instants in a time window to enable monitoring a traffic trend. - 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 system for re-training a traffic density classification engine comprising:
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a. a database to collect a set of misclassified video image data of a video image capturing device from among plurality of video image capturing devices; b. a reinforcement learning engine to train the traffic density classification engine 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 computer program product for use with a computer, the computer program product comprising a computer usable medium having a computer readable program code embodied therein for analyzing on-road traffic density, the computer readable program code storing a set of instructions configured for:
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a. allowing a user to select a video image capturing device from among a plurality of video image capturing devices; b. allowing the user to select coordinates in one of the 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 (ROI); c. processing the ROI, wherein the processing comprises; i. segmenting the ROI into one or more overlapping sub-windows, ii. converting the one or more overlapping sub-windows into one or more feature vectors through textural feature extraction technique; d. generating a traffic confidence value or a no-traffic confidence value for each of the feature vectors by a traffic density classifier; e. computing a traffic density value depending on a number of sub-windows with high traffic and total number of sub-windows within the ROI; f. 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 g. displaying traffic density values at different instants in a time window to enable monitoring of a traffic trend. - View Dependent Claims (48)
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49. A computer program product for use with a computer, the computer program product comprising a computer usable medium having a computer readable program code embodied therein for re-training a traffic density classification engine, the computer readable program code storing a set of instructions configured for:
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a. collecting a set of misclassified video image frames captured by an image capturing device from among a plurality of image capturing devices; and b. 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