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.
seen to the best of our knowledge.
Original language | English |
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Article number | 274 |
Number of pages | 18 |
Journal | Proceedings of the ACM on Human-Computer Interaction |
DOIs | |
Publication status | Accepted/In press - 25 Jul 2020 |
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Luke Chen
- School of Computing - Professor of Data Analytics
- Faculty Of Computing, Eng. & Built Env. - Full Professor
Person: Academic