Abnormal Event Detection in Crowded Video Scenes

Abstract

Intelligent Video Investigation is on nice interest in trade applications because of increasing demand to scale back the force of analyzing the large-scale video information. Sleuthing the abnormal events from crowded video scenes offer varied difficulties. Initially, an oversized variety of moving persons will simply distract the native anomaly detector. Secondly it’s tough to model the abnormal events in real time. Thirdly, the inaccessibility of ample samples of coaching information for abnormal events ends up in problem in sturdy detection of abnormal events. Our planned system provides a peculiar approach to find anomaly in crowded video scenes. We are initially divide the video frame into patches and apply the Difference-of-Gaussian (DoG) filter to extract edges. Then we work out Multiscale Histogram of Optical Flow (MHOF) and Edge directed bar chart (EOH) for every patch. Then exploitation of Normalized Cuts (NCuts) and Gaussian Expectation-Maximization (GEM) techniques, and to cluster the similar patches into cluster and assign the motion context. Finally exploitation of k-Nearest neighbor (k-NN) search, and establish the abnormal activity at intervals in the crowded scenes. Our spatio-temporal anomaly search system helps to boost the accuracy and computation time for detection of irregular patterns. This technique is helpful for investigation, trade specific and market applications like public transportation, enforcement, etc.