Robust and efficient object tracking algorithm under illumination changes based on joint gradient-intensity histogram

Visual tracking under illumination changes is a challenging task for various computer vision systems. In this paper, we propose a robust and efficient tracking algorithm based on a novel feature descriptor called joint gradient-intensity histogram, which exploits both the pixel intensity and gradient orientation features. Unlike the traditional histogram based algorithms, the joint gradient-intensity histogram is extracted efficiently by employing the integral histogram, which is calculated recursively in the specially designed local image region, making the exhaustively searching process in real time. Experiments with the state-of-art tracking methods demonstrate superior performance of the proposed tracking algorithm under illumination changes.