Online multiple instance gradient feature selection for robust visual tracking

In this paper, we focus on learning an adaptive appearance model robustly and effectively for object tracking. There are two important factors to affect object tracking, the one is how to represent the object using a discriminative appearance model, the other is how to update appearance model in an appropriate manner. In this paper, following the state-of-the-art tracking techniques which treat object tracking as a binary classification problem, we firstly employ a new gradient-based Histogram of Oriented Gradient (HOG) feature selection mechanism under Multiple Instance Learning (MIL) framework for constructing target appearance model, and then propose a novel optimization scheme to update such appearance model robustly. This is an unified framework that not only provides an efficient way of selecting the discriminative feature set which forms a powerful appearance model, but also updates appearance model in online MIL Boost manner which could achieve robust tracking overcoming the drifting problem. Experiments on several challenging video sequences demonstrate the effectiveness and robustness of our proposal.

► A gradient-based HOG feature selection under online MIL for robust visual tracking.
► A novel discriminative object appearance model based on HOG rectangle bags.
► An optimization scheme updates appearance model robustly.
► Building a robust discriminative appearance model to avoid drifting problem.
► Experiments show our tracker is more robust and stable than state-of-the-art methods.