Improved EfficientDet Algorithm for Basketball Players’ Upper Limb Movement Trajectory Recognition

Abstract

ABSTRACT Whether a basketball player’s movements are standard or not is a key indicator to evaluate a basketball player and a key point for basketball player training. In order to improve the training effect of basketball players, effective and fast accurate recognition of basketball players’ action trajectories is crucial. In order to solve the problems of inadequate extraction of limb action features and loss of detail information due to multiple scales of limb action features caused by traditional recognition algorithms, an action trajectory recognition algorithm based on the improved EfficientDet-D0 is proposed. The algorithm firstly adds a spatial attention mechanism in the backbone network of EfficientDet-D0, where can locate the limb action trajectory features in the image more accurately. Secondly, in the feature fusion network, in order to describe the high-frequency detail information lost by downsampling, the idea of Laplace pyramid is used to fuse the detail feature maps in the top-down fusion path, and cross-level connections are added to make full use of the feature information of different resolutions, so that the acquired high-level feature map information is richer. Finally, the whole network is trained using the migration learning technique and Adam optimizer. The experimental results show that the model achieves a PCP value (percentage correct parts) of 95.6% on the upper limb pose dataset and takes only 5.34 s for 1000 tests. Compared with the traditional algorithm, it has a higher accuracy rate and stronger robustness, with strong practical applicability, realizing the effective and accurate recognition of basketball players’ upper limb action trajectories in real time.