Abstract: To address the issues of voxel data occupying a large amount of memory space and limited action information that can be extracted by a single network, multiple choice learning (MCL) based multi-rate point cloud action recognition model is proposed. Firstly, the preprocessing method of point cloud data is optimized, reducing the overall size of the point cloud data by half. Secondly, an MCL-based multi-rate point cloud action recognition model is introduced, which takes the MCL framework as the main structure and incorporates confidence loss fuction and generalized distillation. The confidence loss is used to determine the “teacher” and “student” networks during knowledge distillation. The “teacher” network is subjected to generalized distillation to guide the “student” network, enabling information fusion between networks operating at different rates. This model was evaluated on the publicly available MMActvity dataset and Pantomime dataset, achieving accuracies of 91.3% and 95.2%, respectively. The experimental results validate the effectiveness of the proposed model.

Key words: multiple choice learning(MCL), action recognition, voxel data, generalized distillation