FTDMamba: Frequency-Assisted Temporal Dilation Mamba for Unmanned Aerial Vehicle Video Anomaly Detection
Cheng-Zhuang Liu, Si-Bao Chen, Qing-Ling Shu, Chris Ding, Jin Tang, Bin Luo

TL;DR
This paper introduces FTDMamba, a novel network for UAV video anomaly detection that effectively models complex dynamic backgrounds using frequency analysis and multi-scale temporal features, and provides a large new dataset.
Contribution
The paper proposes a new model combining frequency decoupling and temporal dilation for improved UAV VAD, and introduces a large-scale dataset for dynamic background scenarios.
Findings
FTDMamba outperforms existing methods on static benchmarks.
The new MUVAD dataset captures diverse UAV anomalies.
Frequency analysis enhances motion disentanglement in dynamic scenes.
Abstract
Recent advances in video anomaly detection (VAD) mainly focus on ground-based surveillance or unmanned aerial vehicle (UAV) videos with static backgrounds, whereas research on UAV videos with dynamic backgrounds remains limited. Unlike static scenarios, dynamically captured UAV videos exhibit multi-source motion coupling, where the motion of objects and UAV-induced global motion are intricately intertwined. Consequently, existing methods may misclassify normal UAV movements as anomalies or fail to capture true anomalies concealed within dynamic backgrounds. Moreover, many approaches do not adequately address the joint modeling of inter-frame continuity and local spatial correlations across diverse temporal scales. To overcome these limitations, we propose the Frequency-Assisted Temporal Dilation Mamba (FTDMamba) network for UAV VAD, including two core components: (1) a Frequency…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Human Pose and Action Recognition · Video Surveillance and Tracking Methods
