基于超限学习机的深度网络时间分组行为识别  

In-Depth Network Time Grouping Behavior Recognition Based on Over-Limit Larning Mchine

作　　者：裴永强 王家伟[1] 汤雪芹 PEI Yongqiang;WANG Jiawei;TANG Xueqin(College of Information Science and Engineering,Chongqing Jiaotong University,Chongqing 400064,China)

机构地区：[1]重庆交通大学信息科学与工程学院,重庆400064

出　　处：《吉林大学学报（信息科学版）》2020年第6期702-708,共7页Journal of Jilin University（Information Science Edition）

基　　金：重庆市教委科技技术基金资助项目(KJ1717368)。

摘　　要：为准确识别视频目标个体完整行为动作序列,增强视频行为识别精度,提出一种基于超限学习机的深度网络时间分组行为识别方法。首先按照人体行为关键姿态个数明确行为识别模型的状态数量,建立人体运动行为多尺度结构关联,把运动轨迹及边缘轮廓小波矩的不同尺度特征引入行为模型中,获取人体运动行为概略特征;其次利用视频分组稀疏抽样法,将视频分割成等时长分组,运用标准反向传播法优化模型参数,实现低成本视频级时间建模,并确保建模过程信息完整性;最后根据隐含层激活函数输出及对应输出层权重系数,得到灵敏度解析式,按照灵敏度参数对隐含节点进行排序,删除次要节点,实现深度网络时间分组行为的精准识别。仿真实验结果表明,该方法具备较高水准的识别精度,且耗时少,拥有极强的鲁棒性。In order to accurately identify the complete action sequence of video target individuals and enhance the accuracy of video behavior recognition,a deep network time grouping behavior recognition method based on over-limit learning machine is proposed. First,the number of states of the behavior recognition model is delermined according to the number of key human behavior gestures, establish the multi-scale structure association of human motion behavior,and the different scale characteristics of motion trajectories and edge contour wavelet moments are introduced into the behavior model to obtain general characteristics of human motion behavior. Using the video grouping sparse sampling method,the video is divided into equal duration groups,and the standard backpropagation method is used to optimize the model parameters,to realize low-cost video-level time modeling and to ensure the integrity of the modeling process information. Finally according to the hidden layer activation function output and corresponding output layer weight coefficients,sensitivity analytical formula is obtained,hidden nodes are sorted according to sensitivity parameters,minor nodes are deleted,and accurate recognition of deep network time grouping behavior are realized. The results of simulation experiments show that the method has a high level of recognition accuracy,is less time-consuming,and has strong robustness.

关 键 词：正则化 超限学习机 深度网络 行为识别 

分 类 号：TP352[自动化与计算机技术—计算机系统结构]