Visual Tracking Based On Sparse Representation

Visual tracking is a fundamental problem in the field of computer vision, which includes many other advanced technologies such as digital signal processing, pattern recognition, machine learning, data mining, multimedia search and so on. There are broad applications about tracking in both military and civil fields. In recent years, researchers have provided a lot of tracking methods. But how to design a good tracking algorithm which can handle with the appearance changes, occlusion, noise, and scale change is still a challenging problem.In this paper, we review the particle filer algorithm in the Bayes framework and adopt the subspace model based on principal component analysis, sparse representation, multi-task learning, na?ve Bayes classifier to provide three visual tracking methods.1) We extract the Haar-like features from the object and then use the sparse representation to select those features which are good at distinguishing the object and background. In the following step, we train the na?ve Bayes classifier based on positive and negative samples. In the end, we adopt this classifier to track the object and acquire better performance.2) We introduce the subspace model based on PCA into the traditional L1 tracking framework. Also, the incremental PCA method is applied to update the appearance model. So, we can better represent the object. In addition, we view the sparse representation process of each candidate object as one task and use the technology of multi-task learning to mine the relationship among the tasks. Therefore, we improve the tracking accuracy.3) We apply the sparse representation into appearance model and establish a local appearance model based on sparse representation. This model takes full advantages of the coefficients of sparse representation to capture the object and can better distinguish between the object and background. Meantime, the incremental PCA and sparse representation are both used in updating the appearance model and can deal with the occlusion and drift problem. This model with the L1 framework can do a good job.