Fuzzy Least Squares Support Vector Machine With Adaptive Membership for Object Tracking

Fuzzy learning has been introduced into tracking and achieved great success. However, the membership in the existing fuzzy learning based tracking algorithm is fixed, which lacks the adaptivity to measure the importance of the samples. To improve the tracking adaptivity and flexibility, in this paper, we propose a novel tracking method based on fuzzy least squares support vector machine with adaptive membership (FLS-SVM-AM). First, we formulate tracking as an adaptive membership based fuzzy learning problem, which addresses the issue of fixed membership in existing methods and can better measure the importance of the training samples. Second, we present the FLS-SVM-AM method to build the appearance model, and develop an iterative optimization process to solve the FLS-SVM-AM problem. Third, we define a new membership based on the PASCAL VOC overlap rate and exponential function, which is used to measure the importance of different samples more accurately. Experimental results in the benchmark datasets demonstrate that the proposed method not only outperforms the existing fuzzy learning based tracking methods, but also is comparable to many state-of-the-art methods.