3 papers
cs.CV2024
Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos
Rongqin Liang, Yuanman Li, Jiantao Zhou +1
Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems. Previous single-stage TAD method…
cs.CV2023★ 3 cited
A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos
Rongqin Liang, Yuanman Li, Yingxin Yi +2
Identifying traffic accidents in driving videos is crucial to ensuring the safety of autonomous driving and driver assistance systems. To address the potential danger caused by the…
cs.CV2020
Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision
Rongqin Liang, Yuanman Li, Xia Li +3
Predicting human motion behavior in a crowd is important for many applications, ranging from the natural navigation of autonomous vehicles to intelligent security systems of video…