Evaluation of similarity measures for appearance-based multi-camera matching

Visually matching people appearing in different camera views is an essential part of multi-camera tracking, camera hand-over and video-based identity search. The problem is made difficult by large variations in the appearance of subjects both within the same camera view and between cameras, as well...

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Bibliographic Details
Published in2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras pp. 1 - 6
Main Authors Sherrah, J., Kamenetsky, D., Scoleri, T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2011
Subjects
Accuracy
Cameras
Computational modeling
Histograms
Image color analysis
Probes
Shape
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Summary:Visually matching people appearing in different camera views is an essential part of multi-camera tracking, camera hand-over and video-based identity search. The problem is made difficult by large variations in the appearance of subjects both within the same camera view and between cameras, as well as across time. Rather than relying on a single appearance-based matching method, a fusion of visual cues is more compelling. In this work 8 different similarity measures were evaluated encompassing shape, colour, texture and biometric information. The evaluation was performed on hand-labelled data from 4 indoor surveillance cameras. Experiments examined the accuracy of the similarity measures. Results revealed that matching accuracy is good when tracks come from the same camera, but poor when they come from different cameras. Although different measures performed best in different situations, the colour-based measure produced the best results overall.
ISBN:1457717085
9781457717086
DOI:10.1109/ICDSC.2011.6042930