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Human-Machine Cooperative Video Anomaly Detection
Fan Yang (Northwestern Polytechnical University); Zhiwen Yu (Northwestern Polytechnical University); Liming Chen (Ulster University); Jiaxi Gu (Northwestern Polytechnical University); Qingyang Li (Northwestern Polytechnical University); Bin Guo (northwestern polytechnical univ.)

CSCW '20: ACM Conference on Computer-Supported Cooperative Work and Social Computing
Session: Human-AI Collaboration / Images in AI

Abstract
It is still a challenge to detect anomalous events in video sequences in the field of computer vision due to heavy object occlusions, varying crowded densities and complex situations. To address this, we propose a novel human-machine cooperative approach which uses human feedback on anomaly confirmation to inform and enhance video anomaly detection. Specifically, we analyze the spatio-temporal characteristics of sequential frames of a video from the appearance and motion perspective from which spatial and temporal features are identified and extracted. We then develop a convolutional autoencoder neural network to compute an abnormal score based on reconstruction errors. In this process, a group of experts will provide human feedback to a certain proportion of classified frames to be incorporated into the model, and also the final judgment for the event anomalies for training and classification. The proposed approach is evaluated on 3 publicly available surveillance datasets, showing improved accuracy and competitive performance (93.7% AUC) with respect to the best performance (90.6% AUC) of the state-of-the-art approaches. The approach has not been previously seen to the best of our knowledge.

WEB:: https://cscw.acm.org/2020/

Pre-recorded for the ACM ACM Conference on Computer-Supported Cooperative Work and Social Computing, October 17-21, 2020.