We present a novel algorithm that exploits joint optimization of representation and classification for robust tracking in which the goal is to minimize the least-squares reconstruction errors and discriminative penalties with regularized constraints. In this formulation, an object is represented by the sparse coefficients of local patches based on an overcomplete dictionary, and a classifier is learned to discriminate the target object from the background. To locate the target object in each frame, we propose a deterministic approach to solve the optimization problem. We show that the proposed algorithm can be considered as a generalization of several tracking methods with effectiveness. To account for appearance change of the target and the background, the classifier is adaptively updated with new tracking results. Compared with the most recent tracking algorithms based on sparse representation, the proposed formulation has more discriminative power due to the use of background information and is much faster due to the use of deterministic optimization. Qualitative and quantitative experiments on a variety of challenging sequences show favorable performance of the proposed algorithm against several state-of-the-art methods.

We propose an online algorithm based on local sparse representation for robust object tracking. Local image patches of a target object are represented by their sparse codes with an over-complete dictionary constructed online, and a classifier is learned to discriminate the target from the background. To alleviate the visual drift problem often encountered in object tracking, a two-stage algorithm is proposed to exploit both the ground truth information of the first frame and observations obtained online. Different from recent discriminative tracking methods that use a pool of features or a set of boosted classifiers, the proposed algorithm learns sparse codes and a linear classifier directly from raw image patches. In contrast to recent sparse representation based tracking methods which encode holistic object appearance within a generative framework, the proposed algorithm employs a discrimination formulation which facilitates the tracking task in complex environments. Experiments on challenging sequences with evaluation of the state-of-the-art methods show effectiveness of the proposed algorithm.

We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.

Visual prior from generic real-world images can be learned and transferred for representing objects in a scene. Motivated by this, we propose an algorithm that transfers visual prior learned offline for online object tracking. From a collection of real-world images, we learn an overcomplete dictionary to represent visual prior. The prior knowledge of objects is generic, and the training image set does not necessarily contain any observation of the target object. During the tracking process, the learned visual prior is transferred to construct an object representation by sparse coding and multiscale max pooling. With this representation, a linear classifier is learned online to distinguish the target from the background and to account for the target and background appearance variations over time. Tracking is then carried out within a Bayesian inference framework, in which the learned classifier is used to construct the observation model and a particle filter is used to estimate the tracking result sequentially. Experiments on a variety of challenging sequences with comparisons to several state-of-the-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.

This paper proposes a robust tracking algorithm by third-order tensor representation and adaptive appearance modeling. In this method, the target in each video frame is represented by a third-order tensor. This representation preserves the spatial correlation inside the target region and can integrate multiple appearance cues for target description. Based on this representation, a multilinear subspace is learned online to model the target appearance variations during tracking. Compared to other methods, our approach can detect local spatial structure in the target tensor space and fuse information from different feature spaces. Therefore, the learned appearance model is more discriminative when there are significant appearance variations of the target or when the background gets cluttered. Applying the multilinear algebra, our appearance model can efficiently be learned and updated online, without causing high-dimensional data-learning problems. Then, tracking is implemented in the Bayesian inference framework, where a likelihood model is defined to measure the similarity between a test sample and the learned appearance model, and a particle filter is used to recursively estimate the target state over time. Theoretic analysis and experiments compared with other state-of-the-art methods demonstrate the effectiveness of the proposed approach.

This paper proposes a robust visual tracking approach based on saliency selection. In this method, salient patches and their spatial context inside the object region are exploited for object representation and appearance modeling. Tracking is then implemented by a hybrid stochastic and deterministic mechanism, which needs a small number of samples for particle filtering and escapes local minimum in conventional deterministic tracking. As time progresses, the selected salient patches and their spatial context are updated online to adapt the appearance model to both object and environmental changes. We carry out experiments on several challenging sequences and compare our method with the state-of-the-art algorithm to show its improvement in terms of tracking performance.