Abstract
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.
Highlights
Visual object tracking systems have gained continuous attention and focus in the area of computer vision and pattern recognition because they can be applied to various fields, such as robotics, video surveillance, user-centered interaction systems, video communication and compression and augmented reality [1,2,3,4]
We review some of the important milestones in terms of visual object tracking and sparse representation-based modeling
We proposed a structured sparse principal component analysis (PCA)-based visual object tracking incorporating initialization, motion tracking and online dictionary learning and update
Summary
Visual object tracking systems have gained continuous attention and focus in the area of computer vision and pattern recognition because they can be applied to various fields, such as robotics, video surveillance, user-centered interaction systems, video communication and compression and augmented reality [1,2,3,4]. The core technique of visual object tracking in the Bayesian framework aims to robustly estimate the motion state of a target object with a defined appearance model in each frame from given image sequences. We propose a structured sparse principal component analysis (PCA)-based subspace representation to represent the appearance model of the target object effectively and online learning techniques for robust visual object tracking. The proposed structured sparse PCA-based visual object tracking within the Bayesian framework is decomposed into initialization, observation model, motion tracking model and update.
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