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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.00428 |
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| _version_ | 1866909519901097984 |
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| author | Rawat, Deepti Gupta, Keshav Roy, Aryamaan Basu Sarvadevabhatla, Ravi Kiran |
| author_facet | Rawat, Deepti Gupta, Keshav Roy, Aryamaan Basu Sarvadevabhatla, Ravi Kiran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00428 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos Rawat, Deepti Gupta, Keshav Roy, Aryamaan Basu Sarvadevabhatla, Ravi Kiran Computer Vision and Pattern Recognition 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. |
| title | DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.00428 |