Abstract
Target tracking is an important task in computer vision. Now many tracking algorithms have achieved great results. However, several challenges still hinder the development of tracking algorithms, such as abrupt motion, occlusion and so on. In order to use the feature information of the target more effectively and improve the accuracy and robustness of target tracking, a novel model is designed which is different from the previous discriminative component and generative component, and a novel discriminative-generative collaborative appearance model is presented to combine the two components in this paper. First, for the discriminative component, Locality-Constrained Sparse Coding Algorithm is proposed. In this algorithm, the objective function of the local feature of the target spatial information is determined by fusing the pyramid maximum pool and local feature histogram method. The objective function has three important parameters, which are solved by different optimization strategies. Second, for the generative component, the Histogram of Locality-Constrained Feature Algorithm is proposed. In this algorithm, the locality constraint is served to describe the spatial information of the target as a generative appearance model. Each image patch can be approximated by a linear combination of a local coordinate system formed by a dictionary whose elements are cluster centers that contain the most representative model of the target. Third, this paper designs a collaborative target tracking framework based on semi-supervised learning algorithm with locality constraint coding. The framework can quickly and robustly determine the feature information of the tracking region. The proposed algorithm is evaluated on the comprehensive test platform. The experimental results show that our method is more robust and efficient, and the precision and success rate of our algorithm are improved by 5.4% and 4.7%, respectively.
Highlights
The last decade has witnessed the great success of visual tracking, especially in the last five years
Spatial pyramid maximum pooling method has the following three advantages [37]: firstly, it can solve the defects caused by different sizes of input image patches; secondly, the features of feature map are extracted from different point view and fuse again, which shows the robustness of the algorithm; thirdly, it can improve the accuracy of histogram features
A scale-invariant feature transform (SIFT) feature descriptor is extracted from each image patch
Summary
The last decade has witnessed the great success of visual tracking, especially in the last five years. Considering that the running speed of these methods is often limited by solving the 1 norm optimization problem, many machine learning researchers have used locality-constrained linear coding (LLC) [25] to achieve a similar sparsity property as using a few anchor points [26] or the k-nearest neighbors (kNN) selection scheme with high computational efficiency This coding method can ensure that similar candidate samples are associated with similar coefficient vectors or share similar dictionaries so that the appearance information carried by their local dictionaries is used synthetically. This encoding algorithm is a component of some visual tracking framework [27]–[29].
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