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2018 | OriginalPaper | Chapter

Real-Time RGBD Object Tracking via Collaborative Appearance and Motion Models

Authors : Danxian Chen, Zhanming Liu, Hefeng Wu, Jin Zhan

Published in: Computational Intelligence and Intelligent Systems

Publisher: Springer Singapore

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Abstract

Visual object tracking remains an active and challenging topic in computer vision due to a great variety of intricate factors such as illumination variation, object deformation and background clutter. Recent research efforts have achieved impressive success in object tracking, but they commonly have to utilize complicated models requiring high computation cost, which renders these methods hardly suitable for many applications. Considering depth information of the scene can provide effective complement to color images, in this paper, we propose a novel and efficient method for tracking an object in RGBD videos by using collaborative appearance and motion models. Experimental results demonstrate that our method achieves superior tracking performance over several state-of-the-methods while running efficiently.

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Metadata
Title
Real-Time RGBD Object Tracking via Collaborative Appearance and Motion Models
Authors
Danxian Chen
Zhanming Liu
Hefeng Wu
Jin Zhan
Copyright Year
2018
Publisher
Springer Singapore
Book
Computational Intelligence and Intelligent Systems

Print ISBN: 978-981-13-1650-0

Electronic ISBN: 978-981-13-1651-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-13-1651-7_40

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