Abstract
Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In addition, we empirically found that existing approaches that use feature magnitudes to represent the degree of anomalies typically ignore the effects of scene variations, and hence result in sub-optimal performance due to the inconsistency of feature magnitudes across scenes. To address this issue, we propose the Feature Amplification Mechanism and a Magnitude Contrastive Loss to enhance the discriminativeness of feature magnitudes for detecting anomalies. Experimental results on two large-scale benchmarks UCF-Crime and XD-Violence manifest that our method outperforms state-of-the-art approaches.
Original language | English |
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Title of host publication | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
Editors | Brian WILLIAMS, Yiling CHEN, Jennifer NEVILLE |
Publisher | AAAI press |
Pages | 387-395 |
Number of pages | 9 |
ISBN (Electronic) | 9781577358800 |
DOIs | |
Publication status | Published - 26 Jun 2023 |
Externally published | Yes |
Publication series
Name | Proceedings of the AAAI Conference on Artificial Intelligence |
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Publisher | Association for the Advancement of Artificial Intelligence |
Number | 1 |
Volume | 37 |
ISSN (Print) | 2159-5399 |
ISSN (Electronic) | 2374-3468 |
Bibliographical note
Publisher Copyright:Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funding
This work was supported by the Smart Traffic Fund (PSRI/27/2201/PR) funded by the Hong Kong Productivity Council and the Transport Department of HKSAR.