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Basketball Flight Trajectory Tracking using Video Signal Filtering
by
Alturki, Ryan
, Zhang, Botao
, Zhang, Yiheng
, Alshawi, Bandar
in
Adaptive algorithms
/ Algorithms
/ Basketball
/ Corner detection
/ Flight
/ Flight paths
/ Flight training
/ Image enhancement
/ Image filters
/ Kalman filters
/ Particle swarm optimization
/ Position errors
/ Target detection
/ Tracking
/ Trajectory optimization
/ Video signals
2023
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Basketball Flight Trajectory Tracking using Video Signal Filtering
by
Alturki, Ryan
, Zhang, Botao
, Zhang, Yiheng
, Alshawi, Bandar
in
Adaptive algorithms
/ Algorithms
/ Basketball
/ Corner detection
/ Flight
/ Flight paths
/ Flight training
/ Image enhancement
/ Image filters
/ Kalman filters
/ Particle swarm optimization
/ Position errors
/ Target detection
/ Tracking
/ Trajectory optimization
/ Video signals
2023
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Do you wish to request the book?
Basketball Flight Trajectory Tracking using Video Signal Filtering
by
Alturki, Ryan
, Zhang, Botao
, Zhang, Yiheng
, Alshawi, Bandar
in
Adaptive algorithms
/ Algorithms
/ Basketball
/ Corner detection
/ Flight
/ Flight paths
/ Flight training
/ Image enhancement
/ Image filters
/ Kalman filters
/ Particle swarm optimization
/ Position errors
/ Target detection
/ Tracking
/ Trajectory optimization
/ Video signals
2023
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Basketball Flight Trajectory Tracking using Video Signal Filtering
Journal Article
Basketball Flight Trajectory Tracking using Video Signal Filtering
2023
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Overview
During a basketball game, the ball moves are dynamic, and it is very hard for athletes and trainers to track every move of the ball. An accurate image tracking of a basketball flight path provides the basis for basketball training and other applications. The flight trajectory tracking method based on video signal filtering is studied in this paper. Specifically, the adaptive median filtering algorithm is used to filter the basketball flight video signal. After applying median filtering, the image difference is selected to enhance the basketball trajectory flight images, followed by the Harris corner detection algorithm enhancing the images. Moreover, the SURF algorithm is used to extract features of basketball targets according to the detection results of corner points in the images. Finally, the Particle Swarm Optimization algorithm optimizes the basketball flight trajectory tracking results obtained through the Kalman filter algorithm. The experimental results show that the proposed method can accurately track the flight path of basketball, the real rate is 97%, and the maximum difference between the number of frames and the actual result is 1 frame. The position error and the end position error of the tracking result are both less than 5 cm, which is suitable for basketball training and other practical applications.
Publisher
Springer Nature B.V
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