以深度學習為基礎之多人即時動作辨識系統

Deep Learning Based Real-Time Multiple-Person Action Recognition System

蔡仁凱(Tsai Jen-Kai)

指導教授 ： 許陳鑑 ； 王偉彥

國立臺灣師範大學/科技與工程學院/電機工程學系/碩士(2020年)

https://doi.org/10.6345/NTNU202001187

摘要

無資料

關鍵字

動作辨識 ； 深度學習 ； 人物追蹤 ； 智慧型監控 ； 三維卷積 ； 人臉辨識

並列摘要

無資料

並列關鍵字

action recognition ； deep learning ； face recognition ； human tracking ； smart surveillance ； 3D convolution

參考文獻

[1] L. Xia, C. Chen, and J. Aggarwal, “View invariant human action recognition using histograms of 3D joints,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, Jun. 2012, pp. 20-27.

[2] J. Liu, A. Shahroudy, D. Xu, and G. Wang, “Spatio-temporal lstm with trust gates for 3d human action recognition,” in Proc. European Conference on Computer Vision, Springer, Cham, Sep. 2016, pp. 816-833.

[3] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, Jul. 2017, pp. 7291-7299.

[4] H.-S. Fang, S. Xie, Y.-W. Tai, and C. Lu, “RMPE: Regional multi-person pose estimation,” in Proc. IEEE International Conference on Computer Vision (ICCV), Venice, Italy, Oct. 2017, pp. 2334-2343.

[5] J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, Jun. 2015, pp. 2625-2634.