Target tracking is an important field of computer vision. The template matching tracking algorithm based on squared difference matching (SSD) and standard correlation coefficient (NCC) matching is very sensitive to the gray change of image. When the brightness or gray change, the tracking algorithm will be affected by high-frequency information. Tracking accuracy is reduced, resulting in loss of tracking target. In this paper, a differential tracking algorithm based on discrete sine transform is proposed to reduce the influence of image gray or brightness change. The algorithm that combines the discrete sine transform and the difference algorithm maps the target image into a image digital sequence. The Kalman filter predicts the target position. Using the Hamming distance determines the degree of similarity between the target and the template. The window closest to the template is determined the target to be tracked. The target to be tracked updates the template. Based on the above achieve target tracking. The algorithm is tested in this paper. Compared with SSD and NCC template matching algorithms, the algorithm tracks target stably when image gray or brightness change. And the tracking speed can meet the read-time requirement.
To deal with both of the fluctuation of background intensity and the random phase shift error, this paper present an efficient and rapid phase extraction algorithm. The parametric equations of Lissajous ellipse are derived by subtraction operations on three random interferograms. Then the elliptic parameters are calculated by ellipse fitting, which is used for phase extraction. It is unnecessary for the algorithm to calculate the random phase shift value and remove the background term, which reduces the algorithm’s complexity and shortens the processing time. The effectiveness and reliability of the algorithm are verified by both the numerical simulations and the experiment. The results shows that the algorithm is robust to the fluctuation of background intensity and modulation amplitude.