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Indexed by:期刊论文

Date of Publication:2016-11-01

Journal:PATTERN RECOGNITION

Included Journals:SCIE、EI、Scopus

Volume:59

Issue:,SI

Page Number:302-311

ISSN No.:0031-3203

Key Words:Anomaly detection; Locality sensitive hashing filters; Optimal hash function; Online updating

Abstract:In this paper, we propose a novel anomaly detection approach based on Locality Sensitive Hashing Filters (LSHF), which hashes normal activities into multiple feature buckets with Locality Sensitive Hashing (LSH) functions to filter out abnormal activities. An online updating procedure is also introduced into the framework of LSHF for adapting to the changes of the video scenes. Furthermore, we develop a new evaluation function to evaluate the hash map and employ the Particle Swarm Optimization (PSO) method to search for the optimal hash functions, which improves the efficiency and accuracy of the proposed anomaly detection method. Experimental results on multiple datasets demonstrate that the proposed algorithm is capable of localizing various abnormal activities in real world surveillance videos and outperforms state-of-the-art anomaly detection methods. (C) 2015 Elsevier Ltd. All rights reserved.