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In this paper we present a novel visual tracking algorithm, in which object tracking is achieved by using subspace learning and Huber loss regularization in a particle filter framework. The changing appearance of tracked target is modeled by principle component analysis basis vectors and row group sparsity. This method takes advantage of the strengths of subspace representation and explicitly takes the underlying relationship between particle candidates into consideration in the tracker. The representation of each particle is learned via the multi-task sparse learning method. Huber loss function is employed to model the error between candidates and templates, yielding robust tracking. We utilize the alternating direction method of multipliers to solve the proposed representation model. In experiments we tested sixty representative video sequences that reflect the specific challenges of tracking and used both qualitative and quantitative metrics to evaluate the performance of our tracker. The experiment results demonstrated that the proposed tracking algorithm achieves superior performance compared to nine state-of-the-art tracking methods. [ABSTRACT FROM AUTHOR]

This paper proposes an online object tracking algorithm in which the object tracking is achieved by using multi-task sparse learning and non-negative matrix factorization under the particle filtering framework. The object appearance is first modeled by subspace learning to reflect the target variations across frames. Combination of non-negative components is learned from examples observed in previous frames. In order to robust tracking an object, group sparsity constraints are included to the non-negativity one. Furthermore, the alternating direction method of multipliers algorithm is employed to compute the model efficiently. Qualitative and quantitative experiments on a variety of challenging sequences show favorable performance of the proposed algorithm against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Highlights • A method for visual object tracking is proposed. • We propose a decomposition model in visual tracking. • The decomposition model is utilized in an integrated generative model and discriminative model tracking framework. • Alternating direction method of multipliers algorithm guarantees the convergence of the decomposition model. This paper presents a multi-task based object tracking algorithm via a collaborative model. Under the framework of particle filtering, we develop a multi-task sparse learning based generative and discriminative classifier model. In the generative model, we propose a histogram based subspace learning method which takes advantage of adaptive templates update. In the discriminative model, we introduce an effective method to compute the confidence value which assigns more weights to the foreground than the background. A decomposition model is employed to take the common features and outliers of each particle into consideration. The alternating direction method of multipliers (ADMM) algorithm guarantees the optimization problem can be solved robustly and accurately. Qualitative and quantitative comparisons with nine state-of-the-art methods demonstrate the effectiveness and efficiency of our method in handling various challenges during tracking. [ABSTRACT FROM AUTHOR]

In this paper, we formulate particle filter based tracking as a multi-task sparse learning problem that exploits context information. The target and context information which modeled as linear combinations of principal component analysis (PCA) basis is formed as dictionary templates. The target is treated as the guidance and the sampling examples are filtered depending on the similarity between the target and each input. The edge preserving smoothing property of the guided filter is a key factor for object tracking. First, valuable candidates can be selected and the inaccurate candidates become blurry. Therefore, the guided filter can help to distinguish the target from numerous candidates easily. Second, partial occluded target can be recovered by the filtering process via the guidance image. Thus, the drifting problem can be alleviated. Then multi-task sparse learning is employed to learn the target and context information. The proposed learning problem is efficiently solved using an alternating direction method of multipliers (ADMM) method that yields a sequence of closed form updates. We test our tracker on challenging video sequences that involve drastic illumination changes, large pose variations, and heavy occlusions. Experimental results show that our tracker consistently outperforms state-of-the-art trackers. [ABSTRACT FROM AUTHOR]