Automatic Tracking Method of Basketball Flight Trajectory Based on Data Fusion and Sparse Representation Model

Cuiping Cao, Hai Yu, Yun Liu and Shaohui Wang

Complexity, 2021, vol. 2021, 1-9

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9568753

DOI: 10.1155/2021/9568753

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