Hierarchical Human Action Recognition with Self-Selection Classifiers via Skeleton Data<xref rid="ctp_70_5_633fn1" ref-type="fn">*</xref><fn id="ctp_70_5_633fn1"><label>*</label><p>Supported by the National Nature Science Foundation of China under Grant Nos. 11475003, 61603003, and 11471093; the Key Project of Cultivation of Leading Talents in Universities of Anhui Province under Grant No. gxfxZD2016174; Funds of Integration of Cloud Computing and Big Data; Innovation of Science and Technology of Ministry of Education of China under Grant No. 2017A09116; and Anhui Provincial Department of Education Outstanding Top-Notch Talent-Funded Project under Grant No. gxbjZD26</p></fn>

Hierarchical Human Action Recognition with Self-Selection Classifiers via Skeleton Data**

Supported by the National Nature Science Foundation of China under Grant Nos. 11475003, 61603003, and 11471093; the Key Project of Cultivation of Leading Talents in Universities of Anhui Province under Grant No. gxfxZD2016174; Funds of Integration of Cloud Computing and Big Data; Innovation of Science and Technology of Ministry of Education of China under Grant No. 2017A09116; and Anhui Provincial Department of Education Outstanding Top-Notch Talent-Funded Project under Grant No. gxbjZD26

Su Ben-Yue^{1, 2, †}, Wu Huang², Sheng Min^{2, 3}, Shen Chuan-Sheng^{3, ‡}

(Color online) Kinect human model and 5 parts, where Part 1 includes HC, S, SC, and H; Part 2 includes LS, LE, LW, and LH; Part 3 includes RS, RE, RW, and RH; Part 4 includes HL, LK, LA, and LF; Part 5 includes HR, RK, RA, and RF.