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학술지 A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels
Cited 72 time in scopus Download 9 time Share share facebook twitter linkedin kakaostory
저자
무함마드, Arif Mahmood, 신호철, 이승익
발행일
202009
출처
IEEE Signal Processing Letters, v.27, pp.1705-1709
ISSN
1070-9908
출판사
IEEE
DOI
https://dx.doi.org/10.1109/LSP.2020.3025688
협약과제
20HS3500, 실외 무인 경비 로봇을 위한 멀티모달 지능형 정보분석 기술 개발, 신호철
초록
Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community. Compared to the frame-level annotations of anomalous events, obtaining video-level annotations is quite fast and cheap though such high-level labels may contain significant noise. More specifically, an anomalous labeled video may actually contain anomaly only in a short duration while the rest of the video frames may be normal. In the current work, we propose a weakly supervised anomaly detection framework based on deep neural networks which is trained in a self-reasoning fashion using only video-level labels. To carry out the self-reasoning based training, we generate pseudo labels by using binary clustering of spatio-temporal video features which helps in mitigating the noise present in the labels of anomalous videos. Our proposed formulation encourages both the main network and the clustering to complement each other in achieving the goal of more accurate anomaly detection. The proposed framework has been evaluated on publicly available real-world anomaly detection datasets including UCF-crime, ShanghaiTech and UCSD Ped2. The experiments demonstrate superiority of our proposed framework over the current state-of-the-art methods.
KSP 제안 키워드
Anomalous event detection, Carry out, Current state, Deep neural network(DNN), Frame-level, Image and Video Processing, Main network, Pseudo labels, Real-world, Reasoning framework, Self-reasoning