Mean Shift tracking for video moving objects in combination with scale invariant feature transform and Kalman filter

Abstract

To solve the problem of poor tracking performance when the moving target has a relatively large scale change,rotation, fast-moving or occlusion, an object tracking method combining Scale Invariant Feature Transform( SIFT) matching and Kalman filter with the Mean Shift algorithm was put forward. First, the Kalman filter was used to predict the movement state of the moving target and its estimated value was taken as the initial position of Mean Shift tracking. Then, when the measure coefficient for the similarity of the candidate target model and the initial target model was less than a certain threshold, SIFT feature matching was used to look for the possible position of the target and the new candidate target model was built there, meanwhile, the similarity with the initial target model was measured. Finally, by comparing the two matching coefficients, the position associated with a larger one was selected as the target's final position. The experimental results show that the average tracking error of this algorithm is decreased by about twenty percent than the tracking algorithms only combining the SIFT feature or Kalman filter with the Mean Shift alone.