Visual Tracking Based on Particle Filter and Weighted Multiple Instance Learning

Abstract

Visual tracking is one of fundamental tasks in the field of computer vision. In recent years, detection algorithms have been more and more interested in the use of discriminative classifiers for tracking system. Template drift is the major shortcoming of the detection algorithms because of the online self-learning mechanism of the visual tracker. Multiple Instance Learning (MIL) method has been applied to target tracking, which can alleviate the drift to some extent. However, the MIL tracker cannot discriminatively consider the importance of sample and instance in its learning procedure. Particle filter is employed to make the use of the learned classifier and to help generating a better representative set of training samples for the online learning. In this paper, we present a tracking method built by particle filter and weighted MIL tracker, which integrates the sample and instance importance into an efficient online learning procedure when the classifier is being trained. At the end, the experimental results on various videos verify that the proposed method has a satisfaction performance in real-time object tracking.