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학술대회 CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
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저자
무함마드, Arif Mahmood, 마셀라, 이승익
발행일
202008
출처
European Conference on Computer Vision (ECCV) 2020, pp.1-18
DOI
https://dx.doi.org/10.1007/978-3-030-58542-6_22
협약과제
20HS3500, 실외 무인 경비 로봇을 위한 멀티모달 지능형 정보분석 기술 개발, 신호철
초록
Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including 1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. The proposed method obtains 83.03% and 89.67% frame-level AUC performance on the UCF-Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.
KSP 제안 키워드
Anomalous event detection, Clustering distance, Detection Method, Distance-based, Frame-level, Label noise, Real-world, Suppression mechanism, Weakly supervised learning, anomaly detection, existing state