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A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

People’s Re-identification Across Multiple Non-overlapping Cameras by Local Discriminative Patch Matching

Authors : Rabah Iguernaissi, Djamal Merad, Pierre Drap

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

People’s tracking in multi-camera systems is one of the most important parts for the study of human’s behavior. In this work, we propose a re-identification method for associating people across non-overlapping cameras for tracking purposes. The proposed method is based on the use of discriminatives patches (salient regions). Our method is based on the proposal of a new framework that is used for selecting the most discriminative patches for each tracked individual. This framework is based on exploiting both appearance and spatial information to find the most discriminative salient regions. In this framework, each individual is represented by a set of values representing a rough description for several local patches extracted from the given individual. Then, this representation is used to select some interest patches that most represent the individual of interest compared to other individuals. At the end, these patches are used for associating new detected individuals to tracked ones.

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Metadata
Title
People’s Re-identification Across Multiple Non-overlapping Cameras by Local Discriminative Patch Matching
Authors
Rabah Iguernaissi
Djamal Merad
Pierre Drap
Copyright Year
2017
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-59875-8

Electronic ISBN: 978-3-319-59876-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59876-5_22

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