Video anomaly detection based on locality sensitive hashing filters
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. HighlightsWe 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.
Pattern Recognit.