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

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. Project page: https://dash-cop.github.io/