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2017 | OriginalPaper | Chapter

An Integrated Learning Framework for Pedestrian Tracking

Authors : Taihong Xiao, Jinwen Ma

Published in: Intelligent Computing Methodologies

Publisher: Springer International Publishing

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Abstract

Pedestrian tracking has been arguably addressed as a special topic beyond general object tracking. Although many learning or data driven object trackers as well as recent deep learning object trackers have shown excellent performance for general object tracking, they have limited success on pedestrian tracking because there exist three major challenges emerging from pedestrian tracking such as vast variations of human bodies, distraction from similar persons and complete occlusion. In this paper, we propose an integrated learning framework for pedestrian tracking to overcome these problems. It is demonstrated by the experimental results on the SVD-B dataset that our proposed framework can achieve competitive results in comparison with state-of-the-art object trackers under the evaluation of the precision and success rate as well as fps.

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The source code and dataset are available at http://github.com/prinsphield/ILFPT.

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Title: An Integrated Learning Framework for Pedestrian Tracking
Authors: Taihong Xiao
Jinwen Ma
Publisher: Springer International Publishing
Book: Intelligent Computing Methodologies
Print ISBN: 978-3-319-63314-5

Electronic ISBN: 978-3-319-63315-2

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-63315-2_9

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