光学 精密工程, 2017, 25 (3): 799, 网络出版: 2017-04-18   

构建多尺度深度卷积神经网络行为识别模型

Action recognition model construction based on multi-scale deep convolution neural network
作者单位
1 重庆理工大学 计算机学院, 重庆 400054
2 广西师范学院 计算机与信息工程学院, 广西 南宁 530001
引用该论文

刘智, 黄江涛, 冯欣. 构建多尺度深度卷积神经网络行为识别模型[J]. 光学 精密工程, 2017, 25(3): 799.

LIU Zhi, HUANG Jiang-tao, FENG Xin. Action recognition model construction based on multi-scale deep convolution neural network[J]. Optics and Precision Engineering, 2017, 25(3): 799.

参考文献

[1] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection [C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Piscataway, NJ: IEEE, 2005: 886-893.

[2] TIAN Y L, CAO L L, LIU Z C, et al.. Hierarchical filtered motion for action recognition in crowded videos [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2012, 42(3): 313-323.

[3] 张迪飞, 张金锁, 姚克明, 等. 基于SVM分类的红外舰船目标识别[J]. 红外与激光工程, 2016, 45(1): 167-172.

    ZHANG D F, ZHANG J S, YAO K M, et al.. Infrared ship-target recognition based on SVM classification [J]. Infrared and Laser Engineering, 2016, 45(1): 167-172. (inchinese)

[4] LI W, ZHANG Z, LIU Z. Action recognition based on a bag of 3D points [C]. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Piscataway, NJ: IEEE, 2010: 9-14.

[5] WANG J, LIU Z C, WU Y, et al.. Mining actionlet ensemble for action recognition with depth cameras [C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Piscataway, NJ: IEEE., 2012: 1290-1297.

[6] XIA L, AGGARWAL J K. Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera [C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2013: 2834-2841.

[7] OREIFEJ O, LIU Z. Hon4d: histogram of oriented 4D normals for activity recognition from depth sequences [C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2013: 716-723.

[8] ZHANG C Y, TIAN Y L. Edge enhanced depth motion map for dynamic hand gesture recognition [C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway, NJ: IEEE, 2013: 500-505.

[9] YE M, ZHANG Q, WANG L, et al.. A survey on human motion analysis from depth data [J]. Time-of-Flight and Depth Imaging, Sensors, Algorithms, and Applications, Springer, 2013: 149-187.

[10] LE Q V, ZOU W Y, YEUNG S Y, et al.. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis [C]. 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2011: 3361-3368.

[11] ZHANG N, PALURI M, RANZATO M, et al.. Panda: pose aligned networks for deep attribute modeling [C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2014: 1637-1644.

[12] TOSHEV A, SZEGEDY C. Deeppose: human pose estimation via deep neural networks [C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2014: 1653-1660.

[13] LIU P, HAN S, MENG Z, et al.. Facial expression recognition via a boosted deep belief network [C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2014: 1805-1812.

[14] HE K, ZHANG X, REN S, et al.. Spatial pyramid pooling in deep convolutional networks for visual recognition [C]. Computer Vision-ECCV 2014, Springer, 2014: 346-361.

[15] LIN M, CHEN Q, YAN S. Network in network [J]. Computer Science, 2014.

[16] SZEGEDY C, LIU W, JIA Y Q, et al.. Going deeper with convolutions [C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2015: 1-9.

[17] 陈芬, 郑迪, 彭宗举, 等. 基于模式复杂度的深度视频快速宏块模式选择算法[J]. 光学 精密工程, 2014, 22(8): 2196-2204.

    CHEN F, ZHENG D, PENG Z J, et al.. Depth video fast macroblock mode selection algorithm based on mode complexity [J]. Opt. Precision Eng., 2014, 22(8): 2196-2204.(inchinese)

[18] COLLOBERT R, KAVUKCUOGLU K, FARABET C. Torch7: A matlab-like environment for machine learning [R].BigLearn, NIPS Workshop, 2011.

[19] MLLER M, RDER T. Motion templates for automatic classification and retrieval of motion capture data [C]. Proceedings of the 2006 ACM SIGGRAPH, Eurographics Association, 2006: 137-146.

刘智, 黄江涛, 冯欣. 构建多尺度深度卷积神经网络行为识别模型[J]. 光学 精密工程, 2017, 25(3): 799. LIU Zhi, HUANG Jiang-tao, FENG Xin. Action recognition model construction based on multi-scale deep convolution neural network[J]. Optics and Precision Engineering, 2017, 25(3): 799.

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