A Study on the Motion Recognition of Basketball Players Based on Unit Gesture Decomposition

We propose a new approach for the motion recognition of basketball players based on unit gesture decomposition, which can be wildly used for analysis and statistics of the actions of each player. Meanwhile, we analyze the variation of limb angle and extract the characteristics for immediate actions and continuous actions. In the end, due to the divergence of different limbs, we construct two different classifiers corresponding to the upper and lower limbs respectively and obtain the optimal classifier for motion recognition of basketball players by comparing the output of four most popular machine learning algorithms. Our approach tends to have an adequate performance in practice, for recognizing nine different motions of basketball players with average accuracy over 98.85%.