融合局部与全局特征的人体动作识别

doi:10.16182/j.issn1004731x.joss.201807009

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (7): 2497-2506.doi: 10.16182/j.issn1004731x.joss.201807009

融合局部与全局特征的人体动作识别

唐超¹, 张苗辉^2,*, 李伟³, 曹峰⁴, 王晓峰¹, 童晓红⁵

1. 合肥学院计算机科学与技术系, 合肥 230601;
2. 江西省科学院能源研究所, 南昌 330096;
3. 厦门理工学院计算机与信息工程学院, 厦门 360054;
4. 山西大学计算机与信息技术学院, 太原 030006;
5. 合肥职业技术学院信息中心, 合肥 238000

收稿日期:2017-08-14 出版日期:2018-07-10 发布日期:2019-01-08
作者简介:唐超(1977-),男,安徽合肥,博士,讲师,研究方向为机器学习和计算机视觉。
基金资助:
国家自然科学基金(61672204, 41401521, 61602220), 安徽高校优秀拔尖人才培育资助项目(gxfx2017099)

Fusing Local and Global Features for Human Action Recognition

Tang Chao¹, Zhang Miaohui^2,*, Li Wei³, Cao Feng⁴, Wang Xiaofeng¹, Tong Xiaohong⁵

1. Department of Computer Science and Technology, Hefei University, Hefei 230601, China;
2. Energy Research Institute, Jiangxi Academy of Sciences, Nanchang 330096, China;
3. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 360054, China;
4. School of Computer and Information Science, Shanxi University, Taiyuan 030006, China;
5. Information Center, Hefei Technology College, Hefei 238000, China

Received:2017-08-14 Online:2018-07-10 Published:2019-01-08

摘要/Abstract

摘要： 根据视频特征来识别人体行为是一个具有广泛应用的重要研究课题。提出了一种鲁棒性强,抗噪性能优的人体运动目标检测方法和一种简单高效的多信息融合的混合行为特征表示方法和相应的识别算法。该混合行为特征具有简单、鲁棒和判别能力强的特点,它融合了基于中心距的时空兴趣点局部特征和基于曲率函数的傅里叶描述子全局特征,利用泛化能力较强的随机森林模型进行快速分类。实验结果表明,该方法具有简单、快速和高效的特点。

关键词: 人体行为识别, 局部特征, 全局特征, 时空兴趣点, 傅里叶描述子, 随机森林

Abstract: Recognizing human actions according to video features is an important research topic in a wide scope of applications. In this paper, we propose a robust human motion detection method that combines canny operator with the combination of local and global optic flow methods. Meanwhile, this paper presents a simple but efficient action recognition algorithm using fusion visual features. The mixed features fuse two action descriptors, namely centre distance-based space time interest point and curvature function-based Fourier descriptors. The frame-based human action classifier is developed using random forests algorithm. Experimental results show that the proposed method is accurate, efficient and robust compared with other supervised action recognition algorithms.

Key words: human action recognition, local features, global features, space-time interest points, Fourier descriptors, random forest

中图分类号:

TP391

唐超, 张苗辉, 李伟, 曹峰, 王晓峰, 童晓红. 融合局部与全局特征的人体动作识别[J]. 系统仿真学报, 2018, 30(7): 2497-2506.

Tang Chao, Zhang Miaohui, Li Wei, Cao Feng, Wang Xiaofeng, Tong Xiaohong. Fusing Local and Global Features for Human Action Recognition[J]. Journal of System Simulation, 2018, 30(7): 2497-2506.

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融合局部与全局特征的人体动作识别

Fusing Local and Global Features for Human Action Recognition

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