Advantages and disadvantages of target tracking algorithm Meanshift

Original blogger: http://blog.csdn.net/carson2005/article/details/7341051

When the Meanshift algorithm is used for video target tracking, the target's color histogram is used as the search feature, and the iterative meanshift vector makes the algorithm converge to the target's real position, thus achieving the goal of tracking.

The traditional meanshift algorithm has several advantages in tracking:

(1) The calculation of the algorithm is not small, in the target area is known to be able to do real-time tracking;

(2) using the kernel function histogram model, it is insensitive to edge occlusion, target rotation, deformation and background motion.

At the same time, the Meanshift algorithm also has the following disadvantages:

(1) Lack of necessary template updates;

(2) As the width of the window remains constant during the tracking process, the tracking will fail when the target scale changes.

(3) When the target speed is relatively fast, the tracking effect is not good;

(4) The histogram feature is deficient in the description of the target color feature, and lacks the spatial information;

Because of its fast computation speed, it has certain robustness to target deformation and occlusion, so, in the field of target tracking, the meanshift algorithm is still valued by everyone. However, in view of its shortcomings, some improvements and adjustments can be made to the project in practice, for example:

(1) Introduce a certain target location change prediction mechanism, so as to further reduce the search time of meanshift tracking and reduce the computational amount;

(2) A certain way can be used to increase the "characteristics" for target matching;

(3) The fixed bandwidth of kernel function in traditional meanshift algorithm is changed to dynamic bandwidth;

(4) Adopt a certain way to learn and update the whole template;

Advantages and disadvantages of target tracking algorithm Meanshift