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

arXiv: https://arxiv.org/abs/2204.08364 · HTML: https://mlsanity.com/paper/2204.08364/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 18 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments](https://mlsanity.com/paper/1811.10200/) (2018)
- [Dynamic Curriculum Learning for Imbalanced Data Classification](https://mlsanity.com/paper/1901.06783/) (2019)
- [Deep Curriculum Learning for PolSAR Image Classification](https://mlsanity.com/paper/2112.13426/) (2021)
- [Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads](https://mlsanity.com/paper/2109.05226/) (2021)

## Built on by (most cited first, 3 of 3)

- [DashCop: Automated E-ticket Generation for Two-Wheeler Traffic Violations Using Dashcam Videos](https://mlsanity.com/paper/2503.00428/) (2025)
- [Towards Robust Handwritten Text Recognition with On-the-fly User Participation](https://mlsanity.com/paper/2212.08834/) (2022)
- [Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers](https://mlsanity.com/paper/2507.11852/) (2025)

## Abstract (arXiv)

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.