Global Information Guided Video Anomaly Detection
This addresses the problem of reducing annotation effort for video anomaly detection, but it appears incremental as it builds on existing weak-label methods.
The paper tackles video anomaly detection with weak labels by proposing a Global Information Guided framework that mines global patterns and uses spatial reasoning for temporal detection, achieving effectiveness on the CityScene challenge.
Video anomaly detection (VAD) is currently a challenging task due to the complexity of anomaly as well as the lack of labor-intensive temporal annotations. In this paper, we propose an end-to-end Global Information Guided (GIG) anomaly detection framework for anomaly detection using the video-level annotations (i.e., weak labels). We propose to first mine the global pattern cues by leveraging the weak labels in a GIG module. Then we build a spatial reasoning module to measure the relevance between vectors in spatial domain with the global cue vectors, and select the most related feature vectors for temporal anomaly detection. The experimental results on the CityScene challenge demonstrate the effectiveness of our model.