An entropy approach for abnormal activities detection in video streams

• Published in: Pattern recognition Vol. 45; no. 7; pp. 2543 - 2561
• Main Authors: Haidar Sharif, Md, Djeraba, Chabane
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-07-2012; Elsevier
• Subjects: Aberration; Abnormality; Applied sciences; Artificial Intelligence; Circular variance; Computer Science; Counterbalances; Data Structures and Algorithms; Degree of randomness; Entropy; Exact sciences and technology; Frames; Information Retrieval; Information, signal and communications theory; Machine Learning; Multimedia; Randomness; Services and terminals of telecommunications; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Simulation; Streams; Surveillance; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telemetry. Remote supervision. Telewarning. Remote control; Entropy; Circular variance; Degree of randomness; Abnormality; Video signal; Video technique; Remote sensing; Interest region; Image analysis; Simulation; Image sequence; Public health; Remote supervision
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2011.11.023

Detection of aberration in video surveillance is an important task for public safety. This paper puts forward a simple but effective framework to detect aberrations in video streams using Entropy, which is estimated on the statistical treatments of the spatiotemporal information of a set of interest points within a region of interest by measuring their degree of randomness of both directions and displacements. Entropy is a measure of the disorder/randomness in video frame. It has been showed that degree of randomness of the directions (circular variance) changes markedly in abnormal state of affairs and does change only direction variation but does not change with displacement variation of the interest point. Degree of randomness of the displacements has been put in for to counterbalance this deficiency. Simple simulations have been exercised to see the characteristics of these crude elements of entropy. Normalized entropy measure provides the knowledge of the state of anomalousness. Experiments have been conducted on various real world video datasets. Both simulation and experimental results report that entropy measures of the frames over time is an outstanding way to characterize anomalies in videos.
► To detect aberration from real world video streams Entropy has been proposed. ► Degree of randomness of directions and displacements are two core elements of Entropy. ► Entropy measures of video frames over time is a good way to characterize anomalies.