Object tracking based on online representative sample selection via non-negative least square

In the most tracking approaches, a score function is utilized to determine which candidate is the optimal one by measuring the similarity between the candidate and the template. However, the representative samples selection in the template update is challenging. To address this problem, in this paper, we treat the template as a linear combination of representative samples and propose a novel approach to select representative samples based on the coefficient constrained model. We formulate the objective function into a non-negative least square problem and obtain the solution utilizing standard non-negative least square. The experimental results show that the observation module of our approach outperforms several other observation modules under the same feature and motion module, such as support vector machine, logistic regression, ridge regression and structured outputs support vector machine.