-
[pdf]
[supp]
[bibtex]@InProceedings{Majhi_2024_WACV, author = {Majhi, Snehashis and Dai, Rui and Kong, Quan and Garattoni, Lorenzo and Francesca, Gianpiero and Br\'emond, Fran\c{c}ois}, title = {OE-CTST: Outlier-Embedded Cross Temporal Scale Transformer for Weakly-Supervised Video Anomaly Detection}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2024}, pages = {8574-8583} }
OE-CTST: Outlier-Embedded Cross Temporal Scale Transformer for Weakly-Supervised Video Anomaly Detection
Abstract
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.
Related Material