# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 1 Introduction  
Visual tracking is a hot topic in the field of computer vision and has a wide range of applications, such as vision-based control [1], unmanned aerial vehicles [2], intelligent transportation [3], etc. Although some significant progress has been made in recent years, it still remains a challenging problem due to numerous appearance changes caused by factors, such as illumination changes, occlusion, scale variations, shape deformation, etc. Therefore, developing an effective appearance model is the key factor for robust tracking. Specifically, according to the used appearance model, current visual tracking methods can be roughly categorized into two classes: discriminative tracking and generative tracking. In the generative tracking methods, the tracking problem is formulated as a search for regions most similar to the target model, and the information of the target is only used. [PERSON] et al. [4] developed an online subspace learning model to account for appearance variation. In [5], a tracking algorithm based on a visual tracking decomposition scheme was proposed, and the observational model is decomposed into multiple basic observational models to cover a wide range of pose and illumination variation. Motivated by sparse representation in face recognition [6], [PERSON] and [PERSON] [7, 8] formulated tracking as a sparse approximation problem. This method employs holistic representation schemes and hence does not perform well when target objects are heavily occluded. In [9], a tracking method based on the structural local sparse appearance model was proposed. This model exploits both partial information and spatial information, and handles occlusion by pooling across the localpatches. In contrast to just use the target templates and the trivial templates to sparsely represent the target in [7; 8], [PERSON] et al. [10] proposed to represent the target object by a sparse set of target templates together with a sparse set of target weight maps. These weight maps contain the reliable structures of the target object. [PERSON] et al. [11] proposed a multiple-sparse-representation-based tracker which learns the common and feature-specific patterns among multiple sparse representations for appearance modeling. In [12], [PERSON] et al. proposed a unified multi-feature tracking framework based on joint compressive sensing. Their framework can accept features extracted from identical spectral or different spectral images, and provide the flexibility to arbitrarily add or remove features. On the contrary, the discriminative methods treat tracking as a binary classification problem, aiming to find a decision boundary that can best separate the target from the background. Unlike generative methods, the information of both the target and its background is used simultaneously. [PERSON] [13] used Adaboost to combine an ensemble of weak classifiers to form a strong classifier to do tracking. In [14], [PERSON] et al. proposed a semi-supervised online boosting algorithm to handle the drifting problem. [PERSON] et al. [15] proposed an online multiple instance learning (MIL) tracker, where samples are considered within positive and negative bags. [PERSON] et al. [16] introduced a kernelized structured output support vector machine (Struck) for object tracking, which avoids the intermediate step to convert the estimated object position into a set of labeled training examples. Recently, the correlation filter (CF) based discriminative tracking methods have attracted a great deal of attention in tracking community. [PERSON] [17] first introduced the CF to visual tracking. Subsequently, [PERSON] et al. exploited the circulant property of the kernel matrix [18], which was further improved using multi-channel HOG features [19]. [PERSON] et al. [20] integrated adaptive scale estimations with the CF learning. The work of [21] performed complementary tracking that is robust to deformation and illumination variation.