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Found 4 papers, 1 papers with code
Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos
Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems.
A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos
Different from previous approaches, our method can more accurately detect both ego-involved and non-ego accidents by simultaneously modeling appearance changes and object motions in video frames through the collaboration of optical flow reconstruction and future object localization tasks.
STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction
Different from previous approaches, our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors.
Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision
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 surveillance.
Ranked #15 on
Trajectory Prediction
on ETH/UCY
