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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 1: AAAI-22 Technical Tracks 1

Comprehensive Regularization in a Bi-directional Predictive Network for Video Anomaly Detection

February 1, 2023

Authors

Chengwei Chen

East China Normal University


Yuan Xie

East China Normal University


Shaohui Lin

East China Normal University


Angela Yao

National University of Singapore


Guannan Jiang

Contemporary Amperex Technology Co., Limited (CATL)


Wei Zhang

Contemporary Amperex Technology Co., Limited (CATL)


Yanyun Qu

Xiamen University (XMU)


Ruizhi Qiao

Tencent Youtu Lab


Bo Ren

Tencent Youtu Lab


Lizhuang Ma

East China Normal University


Proceedings:

No. 1: AAAI-22 Technical Tracks 1

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision I

Downloads:

Download PDF

Abstract:

Video anomaly detection aims to automatically identify unusual objects or behaviours by learning from normal videos. Previous methods tend to use simplistic reconstruction or prediction constraints, which leads to the insufficiency of learned representations for normal data. As such, we propose a novel bi-directional architecture with three consistency constraints to comprehensively regularize the prediction task from pixel-wise, cross-modal, and temporal-sequence levels. First, predictive consistency is proposed to consider the symmetry property of motion and appearance in forwards and backwards time, which ensures the highly realistic appearance and motion predictions at the pixel-wise level. Second, association consistency considers the relevance between different modalities and uses one modality to regularize the prediction of another one. Finally, temporal consistency utilizes the relationship of the video sequence and ensures that the predictive network generates temporally consistent frames. During inference, the pattern of abnormal frames is unpredictable and will therefore cause higher prediction errors. Experiments show that our method outperforms advanced anomaly detectors and achieves state-of-the-art results on UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets.

DOI:

10.1609/aaai.v36i1.19898


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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