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This paper presents a method of target tracking based on SIFT and Kalman filter. SIFT algorithm has the ability to detect the invariant feature points which used in tracking and Kalman filter has the ability to predict the target location. Firstly, this paper uses SIFT to compute the location of target. Secondly, this paper uses Kalman filter to optimize the target location in order to correct the error of SIFT algorithm precisely. Lastly, this paper uses 2 groups of videos to test this algorithm. The results show that this is an effective tracking method. Keywords-Tracking, SIFT, feature point, Kalman filter

A fast target tracking algorithm based on connected component labeling and grey value statistics is proposed. First of all, nonlinear transformation is applied to perform enhancement for targets; and then adaptive threshold is applied to perform pre-processing division for the images; finally, target characteristics which are extracted through connected component labeling and grey value statistics are used to match the gray, the area and position of target, then pinpointing the target. Experimental results on the MATLAB software platform and the SOPC hardware platform show that it can perform real-time track accurately for the infrared small targets. KeywordsInfrared small target, Connected component labeling, Grey value statistics, Real-time performance