Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection
Trajectory anomaly detection underpins applications from fraud detection to urban mobility analysis. Dense GPS preserves fine-grained evidence such as abnormal speeds and short-duration events, but its quadratic cost makes multi-month analysis intractable; sparse stay-point methods scale by discarding that evidence and require a separate modeling regime. We argue that this bottleneck is unnecessary: dense and sparse trajectories share a natural two-dimensional cyclic structure along within-day and across-day axes. TITAnD (Trajectory Image Transformer for Anomaly Detection) is the first framework to cast both in a single representation, a Hyperspectral Trajectory Image (HTI), a day $\times...
| Authors | Md Awsafur Rahman, Chandrakanth Gudavalli, Hardik Prajapati et al. |
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| Primary category | cs.CV |
| Published | 26 March 2026 |
| Last updated at source | 28 September 2026 |
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