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Indexed by:期刊论文

Date of Publication:2016-03-01

Journal:PATTERN RECOGNITION

Included Journals:SCIE、EI

Volume:51

Page Number:443-452

ISSN No.:0031-3203

Key Words:Anomaly detection; Motion model; Appearance model; Support Vector Data Description (SVDD)

Abstract:In this paper, we present a novel anomaly detection framework which integrates motion and appearance cues to detect abnormal objects and behaviors in video. For motion anomaly detection, we employ statistical histograms to model the normal motion distributions and propose a notion of "cut-bin" in histograms to distinguish unusual motions. For appearance anomaly detection, we develop a novel scheme based on Support Vector Data Description (SVDD), which obtains a spherically shaped boundary around the normal objects to exclude abnormal objects. The two complementary cues are finally combined to achieve more comprehensive detection results. Experimental results show that the proposed approach can effectively locate abnormal objects in multiple public video scenarios, achieving comparable performance to other state-of-the-art anomaly detection techniques. (C) 2015 Elsevier Ltd. All rights reserved.