DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos

Deepti Rawat, Keshav Gupta, Aryamaan Basu Roy, Ravi Kiran Sarvadevabhatla
3/1/2025
cs.CV

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Abstract

Motorized two-wheelers are a prevalent and economical means of transportation, particularly in the Asia-Pacific region. However, hazardous driving practices such as triple riding and non-compliance with helmet regulations contribute significantly to accident rates. Addressing these violations through automated enforcement mechanisms can enhance traffic safety. In this paper, we propose DashCop, an end-to-end system for automated E-ticket generation. The system processes vehicle-mounted dashcam videos to detect two-wheeler traffic violations. Our contributions include: (1) a novel Segmentation and Cross-Association (SAC) module to accurately associate riders with their motorcycles, (2) a robust cross-association-based tracking algorithm optimized for the simultaneous presence of riders and motorcycles, and (3) the RideSafe-400 dataset, a comprehensive annotated dashcam video dataset for triple riding and helmet rule violations. Our system demonstrates significant improvements in violation detection, validated through extensive evaluations on the RideSafe-400 dataset.

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Cite this paper

@article{rawat2025dashcop,
  title  = {DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos},
  author = {Deepti Rawat and Keshav Gupta and Aryamaan Basu Roy and Ravi Kiran Sarvadevabhatla},
  year   = {2025},
  eprint = {2503.00428},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.00428v1}
}

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