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Research on Tracking Technology in Basketball Video
Shengbo Liao,Miao Wang,Haitao Yang 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.1
Tracking of basketball is studied. Firstly, the basketball object template and adaptive object model are established by using the basic characteristic of segmented basketball in the former frame. Then an improved block-matching algorithm is proposed for tracking the basketball. Finally, the edge of basketball is revised, and the mechanism for tracking validity is established.
Research on Scoreboard Detection and Localization in Basketball Video
ShengBo Liao,Yang Wang,Yingxi Xin 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.11
With the development of multimedia technology and the appearance of information highway, the storage and transmission of digital video has become a less difficult issue. Various sport games bring the explosive expanding of sport video, therefore, efficient analysis of sport video is necessary. An algorithm of detection and location for scoreboard is proposed. Firstly, the template of scoreboard is built using multi-dimensional correlation, it involves the location and the detection parameter. Then the scoreboard is located by the template matching.
Research on Long Shot Segmentation in Basketball Video
ShengBo Liao,Jingmeng Sun,Haitao Yang 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.12
In the basketball segmentation in long shots, Gauss filter is adopted to smooth noise in the image firstly. Secondly, the background is separated by the difference of inter-frame and the connected regions are labeled. Thirdly, a strategy is designed to identify basketball by the characteristic of basketball. Finally, the edge deviation is revised and the optimal result is obtained using improved Snakes.
Prediction of Basketball Players' behavior based on Radial Basis Function Neural Network
Shengbo Liao,Deming Zhang,Haitao Yang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.12
An approach based on online RBFNN is proposed to predict the ball-carrier's behavior shooting, passing and dribbling in basketball matches. In order to describe the factors affecting the behavior of ball carrier, artificial potential field (APF)-based player information is introduced to model the court situation of all players after tracking and vision range determination, then a feature vector is formed as the input of the online RBF neural network. The behavior prediction of the ball carrier is solved by the online RBF neural network based on GIRAN learning algorithm. Compared with the offline RBF neural network, the online neural network can adjust both structure and parameters to basketball matches, thus the prediction accuracy is improved to some extent.