Robust Model-Free Multi-Object Tracking with Online Kernelized Structural Learning

One of the most important issues in robust visual tracking is that the method must be flexible enough to endure the inevitable changes in object appearance over time, which is the main propose of many model-free trackers. Nevertheless, existing online model-free methods typically focus on single object tracking. In this letter, we propose a novel multi-object tracker based on online structured learning which allows us to learn a uniform structural classifier from training samples of all objects. We then derive a novel online updating dual form to facilitate efficient non-linear kernels. By formulating a direct online structured learning method for classifying multiple objects, we build a framework for multi-object tracking, where single object tracking is its special case. Both qualitative and quantitative evaluations demonstrate that the proposed multiple object tracker outperforms most current state-of-the-art methods.