Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads

Aman Goyal, Dev Agarwal, Anbumani Subramanian, C.V. Jawahar, Ravi Kiran Sarvadevabhatla, Rohit Saluja; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 4303-4312

Abstract


In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are a significant source of fatalities involving motorcycles. Identifying and penalizing such riders is vital in curbing road accidents and improving citizens' safety. With this motivation, we propose an approach for detecting, tracking, and counting motorcycle riding violations in videos taken from a vehicle-mounted dashboard camera. We employ a curriculum learning-based object detector to better tackle challenging scenarios such as occlusions. We introduce a novel trapezium-shaped object boundary representation to increase robustness and tackle the rider-motorcycle association. We also introduce an amodal regressor that generates bounding boxes for the occluded riders. Experimental results on a large-scale unconstrained driving dataset demonstrate the superiority of our approach compared to existing approaches and other ablative variants.

Related Material


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[bibtex]
@InProceedings{Goyal_2022_CVPR, author = {Goyal, Aman and Agarwal, Dev and Subramanian, Anbumani and Jawahar, C.V. and Sarvadevabhatla, Ravi Kiran and Saluja, Rohit}, title = {Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {4303-4312} }