Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning  

作　　者：Gensheng Hu Min Li Dong Liang Mingzhu Wan Wenxia Bao 

机构地区：[1]Key Laboratory of Intelligent Computing and Signal Processing,Ministry of Education,Anhui University, Hefei 230039,China [2]School of Electronics and Information Engineering,Anhui University,Hefei 230601, China [3]School of Information Science and Technology,Fudan University,Shanghai 200433,China [4]Anhui Key Laboratory of Polarization Imaging Detection Technology,Hefei 230031,China

出　　处：《Journal of Beijing Institute of Technology》2018年第4期592-603,共12页北京理工大学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(61672032,61401001);the Natural Science Foundation of Anhui Province(1408085MF121);the Opening Foundation of Anhui Key Laboratory of Polarization Imaging Detection Technology(2016-KFKT-003)

摘　　要：A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.

关 键 词：video surveillance group activity recognition non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT) Cayley-Klein metric learning 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]