Almost all the online tracking methods suffer from the problem of  drift . The critical reason lies in that they only focus on how to get the most adaptive visual model to the last state(s), but lose the memory of the learned features. Aiming to address this challenging problem, we propose a novel appearance model within the framework of Bayesian-based tracking. The appearance model combines with the most representative basic models learned in the past temporal space. These basic representative appearance models, as the accumulated memory of the different aspects of features from different learning periods, can approximate the target appearance of the coming state as well as possible. Meanwhile, we design methods to online construct and update the basic models. Also, a dynamic scheme is employed to integrate the basic models. The novel model proposed in this paper, by explicit inference, can effectively and efficiently handle the challenging cases such as full occlusion, frequent and drastic appearance variation of the target. As well, it is demonstrated by the extensive experiments.
['Deqian Fu', 'Seong Tae Jhang']
Visual tracking via multiple representative basic appearance models based on l 1 minimization