Robust object tracking algorithm based on sparse eigenbasis
- Author(s): Jing Li 1 and Junzheng Wang 1
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View affiliations
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Affiliations:
1:
Key Laboratory of Complex System Intelligent Control and Decision, School of Automation, Beijing Institute of Technology, Beijing 100081, People's Republic of China
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Affiliations:
1:
Key Laboratory of Complex System Intelligent Control and Decision, School of Automation, Beijing Institute of Technology, Beijing 100081, People's Republic of China
- Source:
Volume 8, Issue 6,
December 2014,
p.
601 – 610
DOI: 10.1049/iet-cvi.2013.0175 , Print ISSN 1751-9632, Online ISSN 1751-9640
To reduce the computation and to improve the performance of object detection and tracking algorithm with object appearance variation, a tracker based on sparse eigenbasis is proposed. According to the compressive sensing theory, the objects are described in a low-dimensional sub-space representation based on Karhunen–Loeve transform learned online. Meanwhile, combining the Bayesian inference, an adaptive object tracker is presented. First, the authors represent the appearance of the object in a low-dimensional sub-space, then the authors obtain the optimal estimation of the state parameters by Bayesian inference. Finally, the authors update the eigenbasis space using the optimal observations. Experimental results show that the proposed method is able to track the objects effectively and robustly under temporary occlusion and large illumination changes.
Inspec keywords: Bayes methods; state estimation; image representation; eigenvalues and eigenfunctions; Karhunen-Loeve transforms; compressed sensing; learning (artificial intelligence); object tracking; inference mechanisms; object detection; image coding
Other keywords: object appearance variation; adaptive object tracker; Bayesian inference; performance improvement; Karhunen-Loeve transform; temporary occlusion; illumination changes; low-dimensional subspace representation; object detection algorithm; optimal observations; compressive sensing theory; online learning; sparse eigenbasis; computation reduction; robust object tracking algorithm; optimal state parameter estimation
Subjects: Integral transforms in numerical analysis; Computer vision and image processing techniques; Image and video coding; Other topics in statistics; Simulation, modelling and identification; Integral transforms in numerical analysis; Linear algebra (numerical analysis); Linear algebra (numerical analysis); Other topics in statistics; Knowledge engineering techniques
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