Visual tracking via adaptive tracker selection with multiple features

Yoon, J, Kim, D and Yoon, K 2012, 'Visual tracking via adaptive tracker selection with multiple features', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7575LNCS, no. PART4, pp. 28-41.


Document type: Journal Article
Collection: Journal Articles

Title Visual tracking via adaptive tracker selection with multiple features
Author(s) Yoon, J
Kim, D
Yoon, K
Year 2012
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume number 7575LNCS
Issue number PART4
Start page 28
End page 41
Total pages 14
Publisher Springer
Abstract In this paper, a robust visual tracking method is proposed to track an object in dynamic conditions that include motion blur, illumination changes, pose variations, and occlusions. To cope with these challenges, multiple trackers with different feature descriptors are utilized, and each of which shows different level of robustness to certain changes in an object's appearance. To fuse these independent trackers, we propose two configurations, tracker selection and interaction. The tracker interaction is achieved based on a transition probability matrix (TPM) in a probabilistic manner. The tracker selection extracts one tracking result from among multiple tracker outputs by choosing the tracker that has the highest tracker probability. According to various changes in an object's appearance, the TPM and tracker probability are updated in a recursive Bayesian form by evaluating each tracker's reliability, which is measured by a robust tracker likelihood function (TLF). When the tracking in each frame is completed, the estimated object's state is obtained and fed into the reference update via the proposed learning strategy, which retains the robustness and adaptability of the TLF and multiple trackers. The experimental results demonstrate that our proposed method is robust in various benchmark scenarios.
Subject Computer Vision
Keyword(s) Appearance learning
Multiple features
Robust likelihood function
Tracker interaction
Transition probability matrix
Visual tracking
DOI - identifier 10.1007/978-3-642-33765-9_3
Copyright notice © Springer-Verlag Berlin Heidelberg 2012
ISSN 0302-9743
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