Change Detection Algorithms for Surveillance in Visual Iot: a Comparative Study.

Beenish Ayesha Akram; AMNA ZAFAR; Ali Hammad Akbar; BILAL WAJID; Shafique Ahmad Chaudhry

Author(s)

Beenish Ayesha Akram Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan

AMNA ZAFAR Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan

Ali Hammad Akbar Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan

BILAL WAJID Department of Electrical Engineering, University of Engineering & Technology, Lahore, Pakistan

Shafique Ahmad Chaudhry Department of Computer Science, Dhofar University, Salalah, Oman

Abstract

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.

Publication Details

Page(s) 77-94

DOI DOI not available

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 37, Issue: 1, Year: 2018

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