In tactical analysis of fast-paced net-based sports such as badminton, the identification of habits and movement of an opponent provides an advantage in terms of reaction time, which results in a player obtaining control of the game. A tactical analyst in badminton segments the court using imaginary lines, and intuitively notates the position of a player according to those segments throughout the game. This paper presents a computer vision based approach to automate this notational process. Motion tracking of the badminton players identifies their position throughout the game. Different court segmentation patterns are tested to identify the borders of the segments to match the intuitive segmentation performed by a badminton tactical analyst. By application of a dynamic window for assigning players to court segments, we achieve an accuracy level of above 85%.
Generalized motion tracking algorithm using competitive learning networks is proposed. Two networks are applied in this algorithm with the input for the network is pixel value of the image. The output from this network is then simulated with second network, whose input is split into blocks with size of N x N.Two stages are involved in this project, namely preparation stage and tracking stage. The preparation stage develops both networks and uses them in tracking stage. Histogram threshold is applied to filter the group numbers of the simulated output. The histogram threshold is improved to enhance the performance of the algorithm. The groups of tracking target are initialized based on the position of the target in first frame. Post-processing, such as image filling is involved in the algorithm.The performance of proposed algorithm shows system robustness on orientation change, size and movement. Hence, feasibility of motion tracking algorithm with competitive learning network is verified as the proposed algorithm is able to locate tracking target in any positions Keywords-varying block size, inverse block processing, varying group size, scoring system
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