基于MCL的多速率点云动作识别  

MCL based multi-rate point cloud action recognition

作　　者：李涛 王松[1] 谢甜 马亚彤 LI Tao;WANG Song;XIE Tian;MA Ya-tong(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,甘肃兰州730070

出　　处：《计算机工程与科学》2024年第11期2035-2044,共10页Computer Engineering & Science

基　　金：国家自然科学基金(62067006);甘肃省自然科学基金(21JR7RA291);甘肃省教育科技创新项目(2021jyjbgs-05)。

摘　　要：针对体素数据会占用大量的内存空间且单网络可提取的动作信息有限的问题,提出了基于MCL的多速率点云动作识别模型。首先,优化了点云数据预处理方法,使点云数据的总体大小减少1/2;其次,提出了基于MCL的多速率点云动作识别模型,以MCL框架为主体结构,引入置信度损失函数和广义蒸馏,通过置信度损失来确定知识蒸馏时的“教师”及“学生”网络;对“教师”网络进行广义蒸馏,对“学生”网络进行指导,实现了不同速率网络之间的信息融合。对该模型在公开的MMActvity数据集和Pantomime数据集上的性能表现进行了评估,分别得到91.3%和95.2%的准确率,实验结果验证了该模型的有效性。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.

关 键 词：MCL 动作识别 体素数据 广义蒸馏 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]