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

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

doi:10.1016/j.cviu.2024.103955

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2024

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Cited by 3 publications

References 33 publications

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Triplet-set feature proximity learning for video anomaly detection

Biradar,

Mandal,

Dube

et al. 2024

Image and Vision Computing

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Triplet-set feature proximity learning for video anomaly detection

Biradar,

Mandal,

Dube

et al. 2024

Image and Vision Computing

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OE-CTST: Outlier-Embedded Cross Temporal Scale Transformer for Weakly-supervised Video Anomaly Detection

Majhi,

Dai,

Kong

et al. 2024

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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Video anomaly detection in real-world scenarios is challenging due to the complex temporal blending of long and short-length anomalies with normal ones. Further, it is more difficult to detect those due to : (i) Distinctive features characterizing the short and long anomalies with sharp and progressive temporal cues respectively; (ii) Lack of precise temporal information (i.e. weak-supervision) limits the temporal dynamics modeling of anomalies from normal events. In this paper, we propose a novel 'temporal transformer' framework for weakly-supervised anomaly detection: OE-CTST † . The proposed framework has two major components: (i) Outlier Embedder (OE) and (ii) Cross Temporal Scale Transformer (CTST). First, OE generates anomaly-aware temporal position encoding to allow the transformer to effectively model the temporal dynamics among the anomalies and normal events. Second, CTST encodes the cross-correlation between multi-temporal scale features to benefit short and long length anomalies by modeling the global temporal relations. The proposed OE-CTST is validated on three publicly available datasets i.e. UCF-Crime, XD-Violence, and IITB-Corridor, outperforming recently reported state-of-the-art approaches.

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