激光与光电子学进展, 2019, 56 (13): 131003, 网络出版: 2019-07-11   

多尺度卷积神经网络的头部姿态估计 下载: 1512次

Head Pose Estimation Based on Multi-Scale Convolutional Neural Network
梁令羽 1,2,3,**张天天 1,3何为 1,*
作者单位
1 中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室, 上海 201800
2 上海科技大学信息科学与技术学院, 上海 200120
3 中国科学院大学, 北京 100049
引用该论文

梁令羽, 张天天, 何为. 多尺度卷积神经网络的头部姿态估计[J]. 激光与光电子学进展, 2019, 56(13): 131003.

Lingyu Liang, Tiantian Zhang, Wei He. Head Pose Estimation Based on Multi-Scale Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131003.

参考文献

[1] Alioua N, Amine A, Rogozan A, et al. Driver head pose estimation using efficient descriptor fusion[J]. EURASIP Journal on Image and Video Processing, 2016, 2016: 2.

[2] Ahn B, Choi D G, Park J, et al. Real-time head pose estimation using multi-task deep neural network[J]. Robotics and Autonomous Systems, 2018, 103: 1-12.

[3] Murphy-Chutorian E, Trivedi M M. Head pose estimation in computer vision: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(4): 607-626.

[4] Zhu XX, RamananD. Face detection, pose estimation, and landmark localization in the wild[C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 2012: 2879- 2886.

[5] DerkachD, RuizA, Sukno FM. Head pose estimation based on 3-D facial landmarks localization and regression[C]∥2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), May 30-June 3, 2017, Washington D. C., USA. New York: IEEE, 2017: 820- 827.

[6] PadelerisP, ZabulisX, Argyros AA. Head pose estimation on depth data based on particle swarm optimization[C]∥2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 2012: 42- 49.

[7] GengX, XiaY. Head pose estimation based on multivariate label distribution[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 1837- 1842.

[8] HuangC, Ding XQ, FangC. Head pose estimation based on random forests for multiclass classification[C]∥2010 20th International Conference on Pattern Recognition, August 23-26, 2010, Istanbul, Turkey. New York: IEEE, 2010: 934- 937.

[9] Drouard V, Horaud R, Deleforge A, et al. Robust head-pose estimation based on partially-latent mixture of linear regressions[J]. IEEE Transactions on Image Processing, 2017, 26(3): 1428-1440.

[10] 夏军, 裴东, 王全州, 等. 融合Gabor特征的局部自适应三值微分模式的人脸识别[J]. 激光与光电子学进展, 2016, 53(11): 111004.

    Xia J, Pei D, Wang Q Z, et al. Face recognition based on local adaptive ternary derivative pattern coupled with Gabor feature[J]. Laser & Optoelectronics Progress, 2016, 53(11): 111004.

[11] 唐云祁, 孙哲南, 谭铁牛. 头部姿势估计研究综述[J]. 模式识别与人工智能, 2014, 27(3): 213-225.

    Tang Y Q, Sun Z N, Tan T N. A survey on head pose estimation[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(3): 213-225.

[12] 桑高丽, 陈虎, 赵启军. 一种基于深度卷积网络的鲁棒头部姿态估计方法[J]. 四川大学学报(工程科学版), 2016, 48(S1): 163-169.

    Sang G L, Chen H, Zhao Q J. Robust head pose estimation based on deep convolution neural networks[J]. Journal of Sichuan University (Engineering Science Edition), 2016, 48(S1): 163-169.

[13] LeCunY, BoserB, Denker JS, et al. Handwritten digit recognition with a back-propagation network[M] ∥Touretzky D S. Advances in neural information processing systems 2. San Francisco: Morgan Kaufmann Publishers Inc., 1990: 396- 404.

[14] IanG, YoshuaB, AaronC. Deep learning[M]. Cambridge: MIT Press, 2016: 203- 204.

[15] 马永杰, 李雪燕, 宋晓凤. 基于改进深度卷积神经网络的交通标志识别[J]. 激光与光电子学进展, 2018, 55(12): 121009.

    Ma Y J, Li X Y, Song X F. Traffic sign recognition based on improved deep convolution neural network[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121009.

[16] 费延佳, 邵枫. 基于图像检索的对比度调整[J]. 激光与光电子学进展, 2018, 55(5): 051002.

    Fei Y J, Shao F. Contrast adjustment based on image retrieval[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051002.

[17] Li HX, LinZ, Shen XH, et al. A convolutional neural network cascade for face detection[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 5325- 5334.

[18] ViolaP, JonesM. Fast and robust classification using asymmetric adaboost and a detector cascade[C]∥Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, December 3-8, 2001, Vancouver, British Columbia, Canada. Cambridge: MIT Press, 2002: 1311- 1318

[19] SzegedyC, LiuW, Jia YQ, et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 7298594.

[20] IoffeS, SzegedyC. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]∥Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 6-11, 2015, Lille, France. Massachusetts: JMLR. org, 2015, 37: 448- 456.

[21] LinM, ChenQ, Yan S. Network in network[EB/OL]. ( 2014-03-04)[2018-11-25]. https:∥arxiv.org/abs/1312. 4400.

[22] Kingma DP, Ba J. Adam: a method for stochastic optimization[EB/OL]. ( 2017-01-30)[2018-11-25]. https:∥arxiv.org/abs/1412. 6980.

[23] HuangC, Ding XQ, FangC. Head pose estimation based on random forests for multiclass classification[C]∥2010 20th International Conference on Pattern Recognition, August 23-26, 2010, Istanbul, Turkey. New York: IEEE, 2010: 934- 937.

[24] 闵秋莎, 刘能, 陈雅婷, 等. 基于面部特征点定位的头部姿态估计[J]. 计算机工程, 2018, 44(6): 263-269.

    Min Q S, Liu N, Chen Y T, et al. Head pose estimation based on facial feature point localization[J]. Computer Engineering, 2018, 44(6): 263-269.

梁令羽, 张天天, 何为. 多尺度卷积神经网络的头部姿态估计[J]. 激光与光电子学进展, 2019, 56(13): 131003. Lingyu Liang, Tiantian Zhang, Wei He. Head Pose Estimation Based on Multi-Scale Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131003.

本文已被 5 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!