Abstract
Considering the limitation of tracking range on sports video target tracking in basketball games, there are some problems, such as poor tracking effect, low accuracy, low anti-interference ability, and being time-consuming. Therefore, this study proposes a target tracking algorithm of basketball video based on improved grey neural network. According to the pixel grey difference of the target image in basketball video, this study applies the adaptive threshold algorithm in order to segment the target image of basketball video and obtain the target area of basketball video. This algorithm can normalize the grey level of the target area, build the generating sequence of the target area, and collect the target data of the basketball video. It obtains the feature output matrix of basketball video target based on the geometric dispersion of the target image and extracts the key feature points of basketball video target by single frame visual difference analysis. In addition, it makes use of the improved grey neural network to track and locate the feature points of basketball video target and reconstruct the basketball video target image with superresolution to realize basketball video target tracking. The experimental results show that the proposed algorithm has good target tracking effect of basketball video, can effectively improve the target tracking accuracy and anti-interference ability, and can shorten the target tracking time.
Highlights
As it is known, the development of computer network technology promotes the update of image processing technology, and the video processing technology is applied in people’s daily lives [1, 2]
In the actual basketball game, it is difficult for the target tracking effect to achieve the ideal state due to the influence of various factors such as illumination, occlusion, and the definition of image acquisition equipment [3]. erefore, the research on target tracking algorithm of basketball video has become a hot issue
Yang and Wang [4] proposed a target tracking algorithm of basketball video based on DSP and FPGA. is study constructed a target index of the basketball video on the basis of collecting the basketball video, initialized the video, and determined the basic color map of the target index. en according to the input basketball image, it used FPGA technology to obtain a new target index image and processed the new target image and the original image to get a new target position, so as to realize the target tracking of basketball video. e algorithm in this study can effectively track moving targets in basketball game video, but the tracking accuracy of moving targets is low
Summary
The development of computer network technology promotes the update of image processing technology, and the video processing technology is applied in people’s daily lives [1, 2]. The image accuracy of basketball video is high, but due to the variable target trajectory of basketball video, the similar moving target, and complex background, it is more difficult to track the target of basketball video. Yang and Wang [4] proposed a target tracking algorithm of basketball video based on DSP and FPGA. E algorithm in this study can effectively track moving targets in basketball game video, but the tracking accuracy of moving targets is low. Wang and Hou [5] proposed a target tracking algorithm of sports video based on optimized particle filter. Aiming at solving the above problems, this study proposes a target tracking algorithm of basketball video based on improved grey neural network, which provides a certain reference for accurately tracking basketball video target
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
