# COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos

arXiv: https://arxiv.org/abs/2101.08463 · HTML: https://mlsanity.com/paper/2101.08463/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 21 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Unsupervised Traffic Accident Detection in First-Person Videos](https://mlsanity.com/paper/1903.00618/) (2019)
- [An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark](https://mlsanity.com/paper/1805.07663/) (2018)

## Built on by (most cited first, 4 of 4)

- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods, Datasets, and Future Directions](https://mlsanity.com/paper/2505.07611/) (2025)
- [STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction](https://mlsanity.com/paper/2308.13183/) (2023)
- [MsFIN: Multi-scale Feature Interaction Network for Traffic Accident Anticipation](https://mlsanity.com/paper/2509.19227/) (2025)

## Abstract (arXiv)

Predicting on-road abnormalities such as road accidents or traffic violations is a challenging task in traffic surveillance. If such predictions can be done in advance, many damages can be controlled. Here in our wok, we tried to formulate a solution for automated collision prediction in traffic surveillance videos with computer vision and deep networks. It involves object detection, tracking, trajectory estimation, and collision prediction. We propose an end-to-end collision prediction system, named as COLLIDE-PRED, that intelligently integrates the information of past and future trajectories of moving objects to predict collisions in videos. It is a pipeline that starts with object detection, which is used for object tracking, and then trajectory prediction is performed which concludes by collision detection. The probable place of collision, and the objects those may cause the collision, both can be identified correctly with COLLIDE-PRED. The proposed method is experimentally validated with a number of different videos and proves to be effective in identifying accident in advance.