A real-time visual object tracking system based on Kalman filter and MB-LBP feature matching

Visual tracking has very important applications in practice. Many proposed visual trackers are not suitable for real-time applications because of their huge computational loads or sensitivities against changing environments such as illumination variation. In this paper, we propose a new tracker which uses modified Multi-scale Block Local Binary Patterns (MB-LBP) like feature to characterize the tracked object. Such feature has low computational load and robustness against illumination variation. An updated appearance model is build based on the modified MB-LBP feature. The model is updated in every frame by replacing the appearance model with the features extracted from the most current detected image patch of target. Moreover, we use the predicted information about the target to constructed a smaller searching area for target in new frame. It greatly reduces computational load for target searching. Numerical experiments show that the drift effect of tracker is greatly avoided and the tracker has very effective and robust performance on various test videos.