# A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure

arXiv: https://arxiv.org/abs/2105.02572 · HTML: https://mlsanity.com/paper/2105.02572/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 66 references that are in the map.
- First posted on arXiv 6 May 2021.

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

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2005.08514/) (2020)
- [TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2007.14189/) (2020)
- [Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network](https://mlsanity.com/paper/2006.12906/) (2020)

## Built on by (most cited first, 7 of 7)

- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [Analyzing vehicle pedestrian interactions combining data cube structure and predictive collision risk estimation model](https://mlsanity.com/paper/2107.12507/) (2021)
- [I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance Cameras](https://mlsanity.com/paper/2211.09342/) (2022)
- [Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurations](https://mlsanity.com/paper/2405.14257/) (2024)
- [DRIFT open dataset: A drone-derived intelligence for traffic analysis in urban environment](https://mlsanity.com/paper/2504.11019/) (2025)
- [A Real-time Evaluation Framework for Pedestrian's Potential Risk at Non-Signalized Intersections Based on Predicted Post-Encroachment Time](https://mlsanity.com/paper/2404.15635/) (2024)
- [Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Boundary Precision](https://mlsanity.com/paper/2507.07460/) (2025)

## Abstract (arXiv)

Road traffic accidents, especially vehicle pedestrian collisions in crosswalk, globally pose a severe threat to human lives and have become a leading cause of premature deaths. In order to protect such vulnerable road users from collisions, it is necessary to recognize possible conflict in advance and warn to road users, not post facto. A breakthrough for proactively preventing pedestrian collisions is to recognize pedestrian's potential risks based on vision sensors such as CCTVs. In this study, we propose a predictive collision risk area estimation system at unsignalized crosswalks. The proposed system applied trajectories of vehicles and pedestrians from video footage after preprocessing, and then predicted their trajectories by using deep LSTM networks. With use of predicted trajectories, this system can infer collision risk areas statistically, further severity of levels is divided as danger, warning, and relative safe. In order to validate the feasibility and applicability of the proposed system, we applied it and assess the severity of potential risks in two unsignalized spots in Osan city, Korea.