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Human Re-identification Through a Video Camera Network

Slawomir Bak 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This thesis targets the appearance-based re-identification of humans in images and videos. Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views, where variations in viewing angle, illumination and object pose, make the problem challenging. We focus on developing robust appearance models that are able to match human appearances registered in disjoint camera views. As encoding of image regions is fundamental for appearance matching, we study different kinds of image descriptors. These different descriptors imply different strategies for appearance matching, bringing different models for the human appearance representation. By applying machine learning techniques, we generate descriptive and discriminative models, which enhance distinctive characteristics of extracted features, improving re-identification accuracy. This thesis makes the following contributions. We propose six techniques for human re-identification. The first two belong to single-shot approaches, in which a single image is sufficient to extract a robust human signature. These approaches divide the human body into the predefined body parts and then extract image features. This allows to establish the corresponding body parts, while comparing signatures. The remaining four methods address the re-identification problem using signatures computed from multiple images (multiple-shot case). We propose two techniques which learn online the human appearance model using a boosting scheme. The boosting approaches improve recognition accuracy at the expense of time consumption. The last two approaches either assume the predefined model, or learn offline a model, to meet time requirements. We find that covariance descriptor is in general the best descriptor for matching appearances across disjoint camera views. As a distance operator of this descriptor is computationally intensive, we also propose a new GPU-based implementation which significantly speeds up computations. Our experiments suggest that mean Riemannian covariance computed from multiple images improves state of the art performance of human re-identification techniques. Finally, we extract two new image sets of individuals for evaluating the multiple-shot scenario.
Mots-clés : ré-identification
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Contributor : Slawomir Bak <>
Submitted on : Monday, December 10, 2012 - 6:30:53 PM
Last modification on : Monday, October 19, 2020 - 11:10:39 AM
Long-term archiving on: : Saturday, December 17, 2016 - 11:50:09 PM


  • HAL Id : tel-00763443, version 1



Slawomir Bak. Human Re-identification Through a Video Camera Network. Computer Vision and Pattern Recognition [cs.CV]. Université Nice Sophia Antipolis, 2012. English. ⟨tel-00763443⟩



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