Online Multiple Instance Joint Model for Visual Tracking

Longyin Wen, Zhaowei Cai, Menglong Yang, Zhen Lei, Dong Yi, Stan Z. Li

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 mechanism. In this paper, a joint model tracker (JMT) is presented, which consists of a generative model based on Multiple Subspaces and a discriminative model based on improved Multiple Instance Boosting (MIBoosting). The generative model utilizes a series of local constructed subspaces to update the Multiple Subspaces model and considers the energy dissipation of dimension reduction in updating 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 various databases validate the effectiveness of our proposed method over other state-of-the-art trackers.