Abstract
The appearance model of flying basketball obtained by the traditional basketball flight trajectory tracking method is not accurate, which leads the anti-interference performance of trajectory tracking not ideal. Based on data fusion and sparse representation model, a new automatic trajectory tracking method is proposed. Firstly, the relevant technologies of basketball flight trajectory automatic tracking are studied and summarized, and then the method is studied. The specific implementation steps of this method are as follows: the features of flying basketball images were extracted by the target feature extraction algorithm, and the appearance model of flying basketball was built based on sparse representation. Data fusion technology and particle filter algorithm are combined to realize automatic tracking of basketball flight path. Through three axial basketball trajectories of automatic tracking test and noise test and verify the design method under the 3D world coordinate system to achieve the X, Y, and Z axis up more accurate tracking, at the same time, after applying measurement signal to noise, automatic trajectory tracking results affected by some, but still managed to realize the trajectory tracking.
Highlights
The target appearance model obtained by the above traditional target tracking methods is not accurate enough, resulting in the unsatisfactory anti-interference performance of trajectory tracking
Referring to the previous research results, this paper introduces data fusion and sparse representation model to realize the automatic tracking of basketball flight trajectory
Based on the three-axis tracking results, it can be found that the output of the basketball flight trajectory automatic tracking algorithm model is very close to the reality; that is, the designed basketball flight trajectory automatic tracking method based on data fusion and sparse representation model can track the basketball flight trajectory more accurately
Summary
In the research of basketball flight trajectory tracking, there are still many problems, such as the difficulty of shooting clear images when the basketball is moving at high speed, the distortion [7] phenomenon of camera imaging, and the air resistance when the ball is flying. Based on the abstraction level, data fusion can be divided into three categories: feature level, decision level, and pixel level. In data fusion, both traditional theories and new technologies are applied. E traditional theories include optimization theory and decision theory, while the new technologies include weighted average method and D.S. Both traditional theories and new technologies are applied. e traditional theories include optimization theory and decision theory, while the new technologies include weighted average method and D.S. [12]
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