Video anomaly detection based on locality sensitive hashing filters
作者：
Highlights：
• We present a locality sensitive hashing filters based method for anomaly detection.
• Normal activities are hashed by hash functions into buckets to build filters.
• Abnormality of a test sample is estimated by filter response of its nearest bucket.
• Online updating mechanism increase the adaptability to scene changes.
• Searching for optimal hash functions improves the detection accuracy.
• Our method performs favorably against previous anomaly detection algorithms.
摘要
Highlights•We present a locality sensitive hashing filters based method for anomaly detection.•Normal activities are hashed by hash functions into buckets to build filters.•Abnormality of a test sample is estimated by filter response of its nearest bucket.•Online updating mechanism increase the adaptability to scene changes.•Searching for optimal hash functions improves the detection accuracy.•Our method performs favorably against previous anomaly detection algorithms.
论文关键词：Anomaly detection,Locality sensitive hashing filters,Optimal hash function,Online updating
论文评审过程：Received 4 July 2015, Revised 8 October 2015, Accepted 20 November 2015, Available online 12 December 2015, Version of Record 23 August 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.11.018
