Binocular visual tracking based on Kalman filter and histogram matching

Visual tracking is a very important problem in the computer vision. Most of the studies focus on the monocular visual tracking. The object tracking methods based on monocular camera would lose the depth information, which will dramatically increase difficulties of tracking when faced with occlusion, shadow and so on. This paper proposes an algorithm of binocular vision tracking based on Kalman filter and histogram matching. This algorithm firstly uses a Kalman filter to predict the position of the target. Then histogram matching is used to search the object in the local region. At last epipolar constraint will be adopted to verify the tracking result, which realizes a complete prediction-matching-verification tracking process. The experiments show that the algorithm could effectively reduce tracking failure and get better tracking performance compared with the Mean Shift algorithm.