'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Although numerous online learning strategies have been proposed to handle the appearance variation in visual tracking, the existing methods just perform well in certain cases since they lack effective appearance learning mech-anism. In this paper, a joint model tracker (JMT) is pre-sented, which consists of a generative model based on Mul-tiple Subspaces and a discriminative model based on im-proved Multiple Instance Boosting (MIBoosting). The gen-erative model utilizes a series of local constructed sub-spaces to update the Multiple Subspaces model and con-siders the energy dissipation of dimension reduction in up-dating step. The discriminative model adopts the Gaussian Mixture Model (GMM) to estimate the posterior probability of the likelihood function. These two parts supervise each other to update in multiple instance way which helps our tracker recover from drift. Extensive experiments on var-ious databases validate the effectiveness of our proposed method over other state-of-the-art trackers. 1
