Learning Representation for Anomaly Detection of Vehicle Trajectories

no code implementations • 9 Mar 2023 • Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato, Xiaowei Yuan, Qi Alfred Chen, Qi Zhu

We conduct extensive experiments to demonstrate that our supervised method based on contrastive learning and unsupervised method based on reconstruction with semantic latent space can significantly improve the performance of anomalous trajectory detection in their corresponding settings over various baseline methods.

Anomaly Detection Autonomous Driving +3