[2603.25255] Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.25255 (cs) [Submitted on 26 Mar 2026] Title:Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection Authors:Md Awsafur Rahman, Chandrakanth Gudavalli, Hardik Prajapati, B. S. Manjunath View a PDF of the paper titled Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection, by Md Awsafur Rahman and 3 other authors View PDF HTML (experimental) Abstract:Trajectory anomaly detection underpins applications from fraud detection to urban mobility analysis. Dense GPS methods preserve fine-grained evidence such as abnormal speeds and short-duration events, but their quadratic cost makes multi-month analysis intractable; consequently, no existing approach detects anomalies over multi-month dense GPS trajectories. The field instead relies on scalable sparse stay-point methods that discard this evidence, forcing separate architectures for each regime and preventing knowledge transfer. We argue this bottleneck is unnecessary: human trajectories, dense or sparse, share a natural two-dimensional cyclic structure along within-day and across-day axes. We therefore propose TITAnD (Trajectory Image Transformer for Anomaly Detection), which reformulates trajectory anomaly detection as a vision problem by representing trajectories as a Hyperspectral Trajectory Image (HTI): a day x time-of-day grid whose channels encode spatial, semantic, temporal, and kinematic information from either m...