Open AccessAnomaly Detection Based on Locality Sensitive Hashing with Genetic Algorithm
Author(s) -
Tao Zhang,
Zijuan Fan,
Xiangyu Yu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012067
Subject(s) - hash function , anomaly detection , computer science , locality sensitive hashing , locality , anomaly (physics) , algorithm , function (biology) , artificial intelligence , genetic algorithm , pattern recognition (psychology) , data mining , hash table , machine learning , computer security , condensed matter physics , evolutionary biology , biology , linguistics , philosophy , physics
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.