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Volume: 29 | Article ID: art00011
Abstract

The goal of the TCD3 project is to identify anomalous and dangerous driving patterns from traffic camera feeds. Successful execution can improve road safety by assisting law enforcement catch dangerous drivers, who text while driving or drink and drive. TCD3—in real time—uses Computer Vision to detect cars on the road, utilizes Machine Learning algorithms to identify cars exhibiting dangerous behaviors, and then notifies law enforcement of suspicious vehicles. The project overcomes several technical challenges such as detecting vehicles under different lighting conditions, tracking vehicles in different frames, and distinguishing random variations in a vehicle's path due to normal driving from anomalous variations due to distracted driving. TCD3's C++ script runs on a server and receives live streaming traffic camera feed. A heuristic Computer Vision algorithm utilizes optical flow analysis, background subtraction, and feature extraction algorithms to reliably determine vehicle positions. A proprietary recursive matrix density-based method was created to clean sensor feeds, sizably improving detection accuracy, and greatly improving on current morphological methods. Image registration allows a vehicle's path to be analyzed through multiple frames. A test suite of traffic camera footage was used to evaluate vehicle detection. Frames were doctored and drunk drivers were simulated to test the Machine Learning system, the algorithm was found to have an 83% accuracy. Machine Learning was used for historical and active comparative analyses of vehicle paths to identify anomaly. The system is contextually aware and is robust with respect to normal irregularities in traffic patterns such as from red lights. Permission for large scale testing of the prototype on actual high fidelity traffic camera footage has been requested. Upon detection, the relevant video clip will be extracted and sent to law enforcement for further action. To increase affordability, processing speed, and scalability, a multinode networked Spark-based supercomputing architecture is being investigated. TCD3 is multi-threaded for maximum resource allocation. The project website is at drunkdriverdetection.com.

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Vidur Prasad, "Traffic Camera Dangerous Driver Detection (TCD3)in Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2017,  pp 67 - 72,  https://doi.org/10.2352/ISSN.2470-1173.2017.9.IRIACV-270

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Copyright © Society for Imaging Science and Technology 2017
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Electronic Imaging
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