2024/10/16 by Chia-Yen Chiang, Yasmin Fathy, Chiang, Chia-Yen +5
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2410.13039
openalex publication_date 2024/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Walking as a form of active travel is essential in promoting sustainable transport. It is thus crucial to accurately predict pedestrian crossing intention and avoid collisions, especially with the advent of autonomous and advanced driver-assisted vehicles. Current research leverages computer vision and machine learning advances to predict near-misses; however, this often requires high computation power to yield reliable results. In contrast, this work proposes a low-complexity ensemble-learning approach that employs contextual data for predicting the pedestrian's intent for crossing. The pedestrian is first detected, and their image is then compressed using skeleton-ization, and contextual information is added into a stacked ensemble-learning approach. Our experiments on different datasets achieve similar pedestrian intent prediction performance as the state-of-the-art approaches with 99.7% reduction in computational complexity. Our source code and trained models will be released upon paper acceptance