Abstract
Mean-Shift is known for its real time and robustness in visual tracking. This is a very good algorithm. In recent years, the algorithm has developed rapidly and has great development prospects. This paper studies Mean-Shift theory and target tracking theory and analyses its advantages in video tracking. By combining the three-frame difference method, nearest neighbour method, and the direction parameter of the target, the Mean-Shift theory based on kernel probability density estimation is studied in the video application in moving target recognition and tracking in sequence images. First, use the prediction method to initially locate the target’s position, and then use the Mean-Shift algorithm to perform iterative calculation to determine the true position of the target. Experiments show that the Mean-Shift algorithm avoids global search, and the improved algorithm introduces the number of iterations, reduces the computational complexity of the algorithm, reduces the time consumption, and ensures the real-time tracking of the algorithm.
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