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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Danxian Chen, Zhanming Liu, Hefeng Wu, Jin Zhan

Erschienen in: Computational Intelligence and Intelligent Systems

Verlag: 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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Metadaten
Titel
Real-Time RGBD Object Tracking via Collaborative Appearance and Motion Models
verfasst von
Danxian Chen
Zhanming Liu
Hefeng Wu
Jin Zhan
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Computational Intelligence and Intelligent Systems

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

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

Copyright-Jahr: 2018

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