Fast Detection of Abnormal Events in Videos with Binary Features
Roberto Leyva, Victor Sanchez, Chang-Tsun Li
Abstract
Millions of surveillance cameras are currently installed in public places around the world, making it necessary to intelligently analyse the acquired data to detect the occurrence of abnormal events. A vast number of methods to detect such events have been recently proposed; unfortunately, there is a lack of methods capable of detecting these events as frames are acquired, also known as online processing. In this paper, we present an online framework for video anomaly detection that employs binary features to encode motion information, and low-complexity probabilistic models for detection. Evaluation results on the popular UCSD dataset and on a recently introduced real-event video surveillance dataset show that our framework outperforms non-online and online methods.
BibTeX
@inproceedings{icassp2018_fastdetectionofa,
title = {Fast Detection of Abnormal Events in Videos with Binary Features},
author = {Roberto Leyva and Victor Sanchez and Chang-Tsun Li},
booktitle = {ICASSP 2018},
year = {2018}
}