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论文类型：期刊论文

发表时间：2016-11-01

发表刊物：PATTERN RECOGNITION

收录刊物：SCIE、EI、Scopus

卷号：59

期号：,SI

页面范围：302-311

ISSN号：0031-3203

关键字：Anomaly detection; Locality sensitive hashing filters; Optimal hash function; Online updating

摘要：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.