Object Tracking Using Local Multiple Features and a Posterior Probability Measure

Abstract

Object tracking has remained a challenging problem in recent years. Most of the trackers can not work well, especially when dealing with problems such as similarly colored backgrounds, object occlusions, low illumination, or sudden illumination changes in real scenes. A centroid iteration algorithm using multiple features and a posterior probability criterion is presented to solve these problems. The model representation of the object and the similarity measure are two key factors that greatly influence the performance of the tracker. Firstly, this paper propose using a local texture feature which is a generalization of the local binary pattern (LBP) descriptor, which we call the double center-symmetric local binary pattern (DCS-LBP). This feature shows great discrimination between similar regions and high robustness to noise. By analyzing DCS-LBP patterns, a simplified DCS-LBP is used to improve the object texture model called the SDCS-LBP. The SDCS-LBP is able to describe the primitive structural information of the local image such as edges and corners. Then, the SDCS-LBP and the color are combined to generate the multiple features as the target model. Secondly, a posterior probability measure is introduced to reduce the rate of matching mistakes. Three strategies of target model update are employed. Experimental results show that our proposed algorithm is effective in improving tracking performance in complicated real scenarios compared with some state-of-the-art methods.

SAFS: Object Tracking Algorithm Based on Self-Adaptive Feature Selection

“…However, there is a problem in the current target update: the drift of the template. In the paper [25], the target template uses the following two methods:…”

“…In the specific algorithm, the two target update methods in paper [25] can be used to enhance the validity of the template update. In the tracking algorithm, the first method described above is used to adaptively adjust the target size.…”

“…In this paper, texture feature is selected as another feature sub-template, which is also a global feature. The original LBP feature operator [25] is chosen to describe the shape information of the object. For the original LBP feature operator with the radius of 1 and sampling points of 8, the 58-dimensional feature vector is obtained by rotation and equivalent transformation.…”

“…where p represents the current frame template; q denotes the target template; φ(p, q) is the similarity of the posterior probability measure for the current frame and the target template; δ is the threshold parameter. If the sub-template needs to be updated, update the vector of the target feature sub-template according to Formula ( 9) [25]:…”