Adaptive background for real-time visual tracking
Visual tracking plays a fundamental role in many applications, such as video surveillance, image compression and three-dimensional reconstruction. From the perspective of accuracy and complexity, correlation filter for target tracking has been proved to be one of the most efficient algorithms. However, it suffers from some difficulties when tracking complex objects with rotations, occlusions and other distractions. To improve its robustness, in this paper, we propose an adaptive background model (ABM) to realize real-time visual tracking with a high adaptivity and accuracy. We use the static background information to help tracking instead of only focusing on the target itself, especially when there are great appearance changes. Moreover, we use peak to side-lobe ratio to update the ABM. As shown in the experiments, our proposed method achieves effective performance in visual tracking with good tradeoff between computational complexity and accuracy.