Abstract
As an important research field of computer vision, there are a lot of achievements of human action recognition in recent Most of the researches are confined to the daily gentle action of human, such as walking jogging, sitting down. In sports video there were many specific characteristics about human actions, such as the upside pose of human, high speed, moving viewpoint. For human motion characteristics of sports video, a highspeed moving target tracking algorithm was proposed. Mean shift was an effective tracking algorithm, which was employed to track moving object in video, but it loses object frequently when the tracked object in the video moves rapidly. We advanced an approach to tracking object moving rapidly, and it can work effectively when the object was in cluttered environment. Experimental shows that the proposed algorithm can improve the tracking effect of mean shift algorithm and effectively track high-speed moving target.
Highlights
Moving object tracking in the video sequence is a process of determining the position of moving objects in sequence images to set the positional relationship between the objects in the current frame image and in the subsequent frame image
As an important research field of computer vision, there are a lot of achievements of human action recognition in recent Most of the researches are confined to the daily gentle action of human, such as walking jogging, sitting down
The moving object tracking in the video sequence can be regarded as a pattern matching process [1, 2]; that is to search corresponding feature pattern in subsequent frame image according to the feature of the current frame image [3]
Summary
Moving object tracking in the video sequence is a process of determining the position of moving objects in sequence images to set the positional relationship between the objects in the current frame image and in the subsequent frame image. 2. Analysis of Mean Shift Tracking Algorithm Assuming that n independent identically distributed sampling points x1 , x2 , .., xn in n dimensional space, the probability density function of f ( x ) is estimated by the kernel function. Analysis of Mean Shift Tracking Algorithm Assuming that n independent identically distributed sampling points x1 , x2 , .., xn in n dimensional space, the probability density function of f ( x ) is estimated by the kernel function When estimates status based on Bayesian theory, if the object model is linear and the noise complies with gauss distribution, Kalman filtering algorithm can be adopted to obtain a set of optimal analytical solutions. The target tracking based on bayesian estimation is the probability distribution function p(Zk | X1:k ) of the target state at k time from the observation sequence X1:K. Assuming the initial state probability density function p(Z0 ) is known, the probability density distribution function p(Zk | X1:k ) can be recursively used to predict and update the two processes
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