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학술대회 Limiting Reconstruction Capability of Autoencoders Using Moving Backward Pseudo Anomalies
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저자
마셀라, 무함마드, 이승익
발행일
202207
출처
International Conference on Ubiquitous Robots (UR) 2022, pp.248-251
DOI
https://dx.doi.org/10.1109/UR55393.2022.9826251
협약과제
22HS1300, 불확실한 지도 기반 실내ㆍ외 환경에서 최종 목적지까지 이동로봇을 가이드할 수 있는 AI 기술 개발, 이재영
초록
Video anomaly detection is one of important components in autonomous surveillance system. However, since anomalous events rarely occurs, it is common to approach this problem using one-class-classification problem in which only normal training data are provided. Typically, an autoencoder (AE) is trained to reconstruct the normal data. As the AE is not trained using the real anomalies, it is expected to poorly reconstruct anomalies in the test time. However, the expectation is often not met as AE can also reconstruct anomalous data as well. Several researchers propose to limit the reconstruction capability of AE using pseudo anomalies constructed from the normal data. In this work, we explore another type of pseudo anomaly, i.e., moving backward. Experiments in two video anomaly detection benchmark datasets, i.e., Ped2 and Avenue, show the effectiveness of our method in limiting the reconstruction capability of AE.
KSP 제안 키워드
Benchmark datasets, Classification problems, One-class classification(OCC), Surveillance system, Test Time, Video anomaly detection, training data