Comparative Performance Analysis of Optical Flow Algorithms for Anomaly Detection
12 Pages Posted: 15 Jul 2019 Last revised: 30 Sep 2019
Date Written: May 18, 2019
Presently there is major interest in visual surveillance systems for crowd anomaly detection. Horn-Schunck, Lucas–Kanade and Farneback optical flow methods are used for the estimation of motion in the scene. The motion vectors; magnitude and orientation are analyzed in this work to detect anomaly in crowd. It helps classify the crowd as normal or abnormal, walking or running, vehicle entering in crowd. Various databases are evaluated to check the validity of the feature extraction process. In order to cluster the behaviour as normal or abnormal, Artificial Neural Network is used as a classifier. The results from Farneback optical flow estimation algorithm are promising for crowd behaviour understanding and anomaly detection.
Keywords: Motion estimation, Optical flow estimation, UCF web, CRCV Dense tracking dataset, UCSD Anomaly Dataset. The University of Minnesota (UMN) database
JEL Classification: Y60
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