Human-Scene Network: A novel baseline with self-rectifying loss for weakly supervised video anomaly detection

Snehashis Majhi, Rui Dai, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca, François Brémond

Abstract: Highlights•A Human-Scene Network to detect human and scene centric divergent video anomalies.•An effective and salient feature combination strategy in decoupled sub-networks.•A self-rectifying loss for better separability among instances in weak-supervision.•The results outperform benchmark methods on many scenarios considered.