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J. Imaging 2018, 4(2), 28;

Exploiting Multiple Detections for Person Re-Identification

Pattern Analysis and Computer Vision (PAVIS), Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
Author to whom correspondence should be addressed.
Received: 18 November 2017 / Revised: 11 January 2018 / Accepted: 11 January 2018 / Published: 23 January 2018
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Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs) to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method. View Full-Text
Keywords: video surveillance; appearance-based re-identification; brightness transfer function; segmentation video surveillance; appearance-based re-identification; brightness transfer function; segmentation

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Bhuiyan, A.; Perina, A.; Murino, V. Exploiting Multiple Detections for Person Re-Identification. J. Imaging 2018, 4, 28.

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