Pedestrian Collision Prediction Using a Monocular Camera

Shiyuan Chen, Xue Qin, Zeyd Boukhers, John See, Wei Sui, Cong Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper introduces a simple yet efficient method, PedView, for pedestrian collision warning in Advanced Driver Assistance Systems (ADAS). In contrast to existing approaches that rely on LiDAR and stereo cameras for pedestrian-vehicle distance calculation, our proposed PedView stands out in three key aspects. Firstly, it leverages a forward-looking monocular camera for 3D pedestrian detection, particularly suitable for resource-limited environments like dealer-installed ADAS. Secondly, PedView goes beyond conventional methods, solely utilizing distance and car speed for collision prediction. Instead, it takes an end-to-end approach by incorporating pedestrian location and intent derived from our proposed 3D detector and fuzzy rules. This integration results in a significant improvement in prediction accuracy. Lastly, extensive experiments conducted on two datasets demonstrate the efficiency of PedView, showcasing its superior performance compared to the discrete conditional rules method (DCR) (Precision 0.937 vs. 0.844 and Recall 0.835 vs. 0.746). These results highlight PedView’s robustness across various real-world scenarios.
Original languageEnglish
Title of host publicationiWOAR '23: Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence
PublisherAssociation for Computing Machinery
ISBN (Print)9798400708169
Publication statusPublished - 11 Oct 2023

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