Instance significance guided multiple instance boosting for robust visual tracking
Jinwu Liu, Yao Lu, Tianfei Zhou · 2016
Multiple Instance Learning (MIL) recently provides an appealing way to alleviate the drifting problem in visual tracking. Following the tracking-by-detection framework, an online MILBoost approach is developed that sequentially chooses weak classifiers by maximizing the bag likelihood. In this paper, we extend this idea towards incorporating the instance significance estimation into the online MILBoost framework. First, instead of treating all instances equally, with each instance we associate a significance-coefficient that represents its contribution to the bag likelihood. The coefficients are estimated by a Bayesian formula that jointly considers the predictions from multiple randomized MILBoost classifiers. Next, we incorporate the estimates within a new boosting procedure for more effectively selection of weak classifiers. Experiments with challenging public datasets show that the proposed method outperforms both existing MIL based and boosting based trackers.