arXiv:2606.14724v1 Announce Type: cross Abstract: Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architectures, but rarely both. We present VigilFormer, a unified framework that combines deformable spatio-temporal attention with causal temporal modeling to detect anomalies in untrimmed surveillance video. The proposed Deformable Spatio-Temporal Encoder (DSTE) attends to a sparse set of informative locations across frames, avoiding
The continuous drive for more efficient and accurate AI in real-time video analysis necessitates innovative architectural solutions like VigilFormer to balance performance and computational load, especially as surveillance needs grow more complex.
This development in deformable attention and causal risk inference could significantly enhance the capabilities of autonomous surveillance systems, reducing false positives and improving response times in critical security applications.
The ability to accurately detect anomalies in untrimmed surveillance video with high throughput marks a step towards more reliable and scalable autonomous monitoring, potentially lowering operational costs and improving situational awareness.
- · Defence & Security sector
- · Smart City infrastructure providers
- · AI Vision software developers
- · Surveillance hardware manufacturers
- · Traditional surveillance monitoring services
- · Legacy AI vision systems (resource intensive)
- · Human anomaly detection operators (for routine tasks)
Improved real-time anomaly detection in surveillance leads to faster identification of security threats and operational issues.
Enhanced autonomous monitoring capabilities could decrease the need for constant human oversight in large-scale surveillance environments, reallocating human resources to response and analysis.
The widespread adoption of such robust AI surveillance systems could raise privacy concerns and ethical debates regarding pervasive automated monitoring.
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Read at arXiv cs.AI