Real-Time Visual Tracking Using a New Weight Distribution

This paper presents a real-time visual tracking algorithm which uses a new weight distribution for color space. Firstly, first-order Kalman filter model is introduced to update video backgrounds and obtain the targets. HSV color space is used to measure the similarity between the supposed targets and match targets. In this process, a weighting function based on pixel confidence and pixel position is proposed to weigh the pixel values in the rectangle area of tracking. The experimental results show that the algorithm is robust to scale invariant, partial occlusion and interactions of non-rigid objects, especially similar objects. The proposed algorithm is computationally efficient and it can satisfy the real-time requirements for visual tracking.