基于时空方向主成分直方图的人体行为识别 下载: 1077次
徐海洋, 孔军, 蒋敏, 昝宝锋. 基于时空方向主成分直方图的人体行为识别[J]. 激光与光电子学进展, 2018, 55(6): 061009.
Haiyang Xu, Jun Kong, Min Jiang, Baofeng Zan. Action Recognition Based on Histogram of Spatio-Temporal Oriented Principal Components[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061009.
[1] 董珂. 基于Kinect的人体行为识别研究[D]. 武汉: 武汉科技大学, 2015.
DongK. Human action recognition based on Kinect[D]. Wuhan: Wuhan University of Science and Technology, 2015.
[2] 蔡加欣, 冯国灿, 汤鑫, 等. 基于姿势字典学习的人体行为识别[J]. 光学学报, 2014, 34(12): 1215002.
[3] RahmaniH, MahmoodA, Huynh DQ, et al. Real time human action recognition using histograms of depth gradients and random decision forests[C]. 2014 IEEE Winter Conference on Application of Computer Vision, 2014: 626- 633.
[4] 蔡加欣, 冯国灿, 汤鑫, 等. 基于局部轮廓和随机森林的人体行为识别[J]. 光学学报, 2014, 34(10): 1015006.
[5] TangS, WangX, LvX, et al. Histogram of oriented normal vectors for object recognition with a depth sensor[C]. Asian Conference on Computer Vision, 2013: 525- 538.
[6] 黄成挥. 基于视频的人体行为识别算法研究[D]. 成都: 电子科技大学, 2016.
Huang CH. Human action recognition algorithm based on video[D]. Chengdu: University of Electronic Science and Technology of China, 2016.
[7] 王军. 基于多示例学习法的人体行为识别[J]. 信息技术, 2016( 7): 65- 70.
WangJ. Human action recognition based on sample learning method[J]. Information Technology, 2016( 7): 65- 70.
[8] 刘智, 董世都. 利用深度视频中的关节运动信息研究人体行为识别[J]. 计算机应用与软件, 2017, 34(2): 189-192.
Liu Z, Dong S D. Human action recognition based on the joint movement in the depth of video information[J]. Computer Applications and Software, 2017, 34(2): 189-192.
[9] 张燕君, 王会敏, 付兴虎, 等. 基于粒子群支持向量机的钢板损伤位置识别[J]. 中国激光, 2017, 44(10): 1006006.
[10] 刘峰, 沈同圣, 马新星. 特征融合的卷积神经网络多波段舰船目标识别[J]. 光学学报, 2017, 37(10): 1015002.
[11] 毕立恒, 刘云潺. 基于改进神经网络算法的植物叶片图像识别研究[J]. 激光与光电子学进展, 2017, 54(12): 121102.
[12] RahmaniH, MahmoodA, Du QH, et al. HOPC: histogram of oriented principal components of 3D pointclouds for action recognition[C]. European Conference on Computer Vision, 2014: 742- 757.
[13] 曹林. 人脸识别与人体动作识别技术及应用[M]. 北京: 电子工业出版社, 2015.
CaoL. Face recognition and human motion recognition technology and application[M]. Beijing: Electronic Industry Press, 2015.
[14] LiW, ZhangZ, LiuZ. Action recognition based on a bag of 3D points[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, 2010: 9- 14.
[15] JiX, ChengJ, FengW. Spatio-temporal cuboid pyramid for action recognition using depth motion sequences[C]. Eighth International Conference on Advanced Computational Intelligence, IEEE, 2016: 208- 213.
[17] WangC, WangY, Yuille AL. An Approach to Pose-Based Action Recognition[C]. Computer Vision and Pattern Recognition IEEE, 2013: 915- 922.
[19] KongY, SattarB, FuY. Hierarchical 3D kernel descriptors for action recognition using depth sequences[C]. IEEE International Conference on Automatic Face and Gesture Recognition, 2015: 1- 6.
[20] ChenC, JafariR, KehtarnavazN. Action Recognition from Depth Sequences Using Depth Motion Maps-based Local Binary Patterns[C]. IEEE Applications of Computer Vision, 2015: 1092- 1099.
[21] OreifejO, LiuZ. HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences[C]. Computer Vision and Pattern Recognition, IEEE, 2013: 716- 723.
徐海洋, 孔军, 蒋敏, 昝宝锋. 基于时空方向主成分直方图的人体行为识别[J]. 激光与光电子学进展, 2018, 55(6): 061009. Haiyang Xu, Jun Kong, Min Jiang, Baofeng Zan. Action Recognition Based on Histogram of Spatio-Temporal Oriented Principal Components[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061009.