摘要
为实现互联网上大量背景复杂、视点变化的视频中人体动作的识别,提出了一种使用无监督的深度信念网络(DBNs)进行人体动作识别的创新方法。该方法采用深度信念网络(DBNs)和受限玻耳兹曼机进行无约束视频的动作识别,利用无监督深度学习模型自动提取合适的特征表示,不需要任何先验知识。在一个具有挑战性的UCF体育数据集上进行实验,证明了该方法准确有效。同时该方法也适用于其他视觉识别任务,并在未来可扩展到非结构化的人体活动识别。
In order to solve the problem of human action recognition in a large number of videos with complex backgrounds and changing viewpoints on the Internet,an innovative method for human action recognition using unsupervised deep belief networks(DBNs)is proposed.This method uses deep belief networks(DBNs)and restricted Boltzmann machines for unconstrained video action recognition,and uses an unsupervised deep learning model to automatically extract appropriate feature representations without any prior knowledge.Through the identification of a challenging UCF sports data set,the method is proved to be accurate and effective.At the same time,this method is suitable for other visual recognition tasks,and will be extended to unstructured human activity recognition in the future.
作者
洪耀球
HONG Yao-qiu(School of Information Engineering,Jingdezhen University,Jingdezhen,Jiangxi 333000,China)
出处
《计算机科学》
CSCD
北大核心
2021年第S02期400-403,共4页
Computer Science
基金
国家自科基金地区科学基金项目(41761012)。
关键词
深度信念网络
玻耳兹曼机
人体动作识别
无监督
Deep belief networks
Boltzmann machine
Human action recognition
Unsupervised
