Context-aware Video Anomaly Detection in Long-Term Datasets

Zhengye Yang, Richard J. Radke; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4002-4011

Video anomaly detection research is generally evaluated on short isolated benchmark videos only a few minutes long. However in real-world environments security cameras observe the same scene for months or years at a time and the notion of anomalous behavior critically depends on context such as the time of day day of week or schedule of events. Here we propose a context-aware video anomaly detection algorithm Trinity specifically targeted to these scenarios. Trinity is especially well-suited to crowded scenes in which individuals cannot be easily tracked and anomalies are due to speed direction or absence of group motion. Trinity is a contrastive learning framework that aims to learn alignments between context appearance and motion and uses alignment quality to classify videos as normal or anomalous. We evaluate our algorithm on both conventional benchmarks and a public webcam-based dataset we collected that spans more than three months of activity.