Anomaly Detection Based on Locality Sensitive Hashing with Genetic Algorithm

Abstract

With the increase of public safety awareness, video anomaly detection has attracted researchers’ attention. In the paper, a novel approach is proposed to detect anomalies in the video. It is based on Locality Sensitive Hashing (LSH), which maps similar data to the same bucket with high probabilities, and non-similar data is mapped to the same bucket with a low probability to detect abnormal videos that are not similar to normal videos. In order to improve the probability of similar data mapping into the same bucket, the Genetic Algorithm (GA) is used to optimize the entire hash function group while maintaining the diversity of the hash function group. The algorithm gets AUC 0.78 on the dataset UCSD ped1 and AUC 0.94 on the dataset UCSD ped2, which confirmed the effectiveness of the algorithm.