DeCoRTAD: Diffusion Based Conditional Representation Learning for Online Trajectory Anomaly Detection

Online trajectory anomaly detection has become a critical task in many real-world applications. However, most existing works assume anomalies are significantly different from normal patterns or require knowing the destinations in advance. In this work, we focus on the problem of detecting anomalous subtrajectories in an online manner without knowing their destinations. This task presents a significant challenge as anomalous subtrajectories may largely overlap with normal trajectories, and we only have limited information on an ongoing trajectory during online detection. To overcome the limitations of current methods, we propose a novel diffusion-based conditional representation learning for online trajectory anomaly detection (DeCoRTAD), that aims to detect anomalies at the representation level, thereby improving computational efficiency. Our framework integrates a diffusion model with two encoders: one for capturing current information and another for encoding historical context. By conditioning the current encoder on the history encoder, we leverage past information as prior knowledge to achieve a more meaningful and compact latent space representation. Our method excels in capturing the normal representation in highly diverse trajectory data, therefore achieving great performance in online detection of fine-grained anomalies. Our experiments show that DeCoRTAD can achieve outstanding online anomaly detection performance in F1 score with an average improvement of 9.31%, and a maximum improvement of 22% over comparable baselines.