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Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning

1
National Institute for Mathematical Science, 70 Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon 34047, Korea
2
Artificial Intelligence Research Institute, 22, Daewangpangyo-ro 712beon-gil, Bundang-gu, Seongnam-si 463400, Gyeonggi-do, Korea
3
College of Computer Science, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(10), 3513; https://doi.org/10.3390/s18103513
Received: 19 July 2018 / Revised: 11 October 2018 / Accepted: 15 October 2018 / Published: 18 October 2018
(This article belongs to the Special Issue Audio–Visual Sensor Fusion Strategies for Video Content Analytics)
Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional descriptors using the standard sparse representation approach. Therefore, this study proposes a structured sparse principal component analysis to represent the complex appearance descriptors of the target object effectively with a linear combination of a small number of elementary atoms chosen from an over-complete dictionary. Using an online dictionary for learning and updating by selecting similar dictionaries that have high probability makes it possible to track the target object in a variety of environments. Qualitative and quantitative experimental results, including comparison to the current state of the art visual object tracking algorithms, validate that the proposed tracking algorithm performs favorably with changes in the target object and environment for benchmark video sequences. View Full-Text
Keywords: visual object tracking structured sparse PCA; appearance model; online learning; structured visual dictionary visual object tracking structured sparse PCA; appearance model; online learning; structured visual dictionary
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Yoon, G.-J.; Hwang, H.J.; Yoon, S.M. Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning. Sensors 2018, 18, 3513.

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