Skeleton Based Action Recognition with Convolutional Neural Network | |
Du, Yong1,3; Fu, Yun4; Wang, Liang1,2,3 | |
2015 | |
会议名称 | Asian Conference on Pattern Recognition |
会议录名称 | Asian Conference on Pattern Recognition |
会议日期 | 2015-11 |
会议地点 | Kuala Lumpur, Malaysia |
摘要 | Temporal dynamics of postures over time is crucial for sequence-based action recognition. Human actions can be represented by the corresponding motions of articulated skeleton. Most of the existing approaches for skeleton based action recognition model the spatial-temporal evolution of actions based on hand-crafted features. As a kind of hierarchically adaptive filter banks, Convolutional Neural Network (CNN) performs well in representation learning. In this paper, we propose an end-to-end hierarchical architecture for skeleton based action recognition with CNN. Firstly, we represent a skeleton sequence as a matrix by concatenating the joint coordinates in each instant and arranging those vector representations in a chronological order. Then the matrix is quantified into an image and normalized to handle the variable-length problem. The final image is fed into a CNN model for feature extraction and recognition. For the specific structure of such images, the simple max-pooling plays an important role on spatial feature selection as well as temporal frequency adjustment, which can obtain more discriminative joint information for different actions and meanwhile address the variable-frequency problem. Experimental results demonstrate that our method achieves the state-of-art performance with high computational efficiency, especially surpassing the existing result by more than 15 percentage on the challenging ChaLearn gesture recognition dataset. |
关键词 | Skeleton Based Action Recognition Convolutional Neural Network |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/11696 |
专题 | 智能感知与计算 |
通讯作者 | Wang, Liang |
作者单位 | 1.Center for Research on Intelligent Perception and Computing, CRIPAC 2.Center for Excellence in Brain Science and Intelligence Technology, CEBSIT 3.Nat’l Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 4.College of Engineering, College of Computer and Information Science, Northeastern University, USA |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Du, Yong,Fu, Yun,Wang, Liang. Skeleton Based Action Recognition with Convolutional Neural Network[C],2015. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ACPR.pdf(487KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论