2 Fig D human model. 1 Fig. 1 The flow of proposed method )9)10) 2.2 3)4)7) 5)11)12)13)14) TOF 1 3 TOF 3 2 c 2011 Information

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1 1 1 2 TOF 2 (D-HOG HOG) Recall D-HOG 0.07 HOG 0.16 Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata 1 and Hironobu Fujiyoshi 1 A method for estimating the pose of a human from depth image by using regression analysis is proposed. With conventional pose estimation methods that use appearance features, it is sometimes difficult to obtain correct results because only two-dimensional information is used. The proposed method uses depth information acquired from a TOF camera to achieve highly accurate pose estimation. For effective use of the depth information, we propose the Depth Difference Feature(DDF). Because the DDF is calculated as a difference is the average distance of two regions, it can be used to distinguish the body from occluding objects and the background behind the body. A comparison of accuracy with the results obtained by the conventional method using appearance features (D-HOG and HOG features) confirmed that the mean recall for the proposed method was 0.07 better than D-HOG and 0.16 better than HOG. 1. CG 1) HOG 2)3)4) 5)6) 5)7)8) Jamie 5) 50 3 TOF Chubu University 1 c 2011 Information Processing Society of Japan

2 2 Fig D human model. 1 Fig. 1 The flow of proposed method )9)10) 2.2 3)4)7) 5)11)12)13)14) TOF 1 3 TOF 3 2 c 2011 Information Processing Society of Japan

4 Fig. 4 Division of block. 3 Fig. 3 Examples of training sample. 3.1 3 3 2 3 19 1 (x, y, z) 3 1 19 3 = 57 1m 4m 3 57 3 3 3.2 TOF TOF LED TOF MESA SR-4000 SR-4000 0.3m 5.

3 4 Fig. 4 Division of block. 3 Fig. 3 Examples of training sample (x, y, z) = 57 1m 4m TOF TOF LED TOF MESA SR-4000 SR m 5.0m TOF 2 TOF ( [pixel]) 15) [pixel] (16 16[pixel]) ( 4 ) 16 16[pixel] M 32 D 5 2 (1) ( 1 N D(i, j) = N n=1 d i n ) ( 1 N N n=1 d j n ) N d i j D = {D(i, j)} i=1,2,...,m 1,j=2,3,...,M 6 (1) 3 c 2011 Information Processing Society of Japan

A = (X T X) 1 X T Y (3) A 3.4 TOF X A Y Y (4) 5 Fig. 5 Depth difference feature.

6 6 Examples of difficult situation by conventional method. 6 3.3 496 x = (x 1, x 2,.

4 A = (X T X) 1 X T Y (3) A 3.4 TOF X A Y Y (4) 5 Fig. 5 Depth difference feature. Y = A X (4) Recall( ) 7 3 Fig. 6 6 Examples of difficult situation by conventional method x = (x 1, x 2,..., x 496 ) 57 y = (y 1, y 2,..., y 57 ) n 496 n X = (x 1, x 2,..., x n) T 57 n Y = (y 1, y 2,..., y n) T (2) A := arg min AX Y 2 (2) A (3) A true positive total pixel (5) Recall Recall = true positive total pixel Recall (5) 4 c 2011 Information Processing Society of Japan

Fig. 7 7 Evaluation method. Fig. 8 8 Precision for number of training sample. 19 E (6) E = 1 N N n=1 (x n x n) 2 + (y n y n) 2 + (z n z n) 2 (6) 1 Recall Table 1 Average recall of each actions.

5 Fig. 7 7 Evaluation method. Fig. 8 8 Precision for number of training sample. 19 E (6) E = 1 N N n=1 (x n x n) 2 + (y n y n) 2 + (z n z n) 2 (6) 1 Recall Table 1 Average recall of each actions. DDF D-HOG HOG WAVE WALK RUN N 19 (x, y, z ) (x, y, z) (HOG D-HOG) (DDF) HOG (HOG) HOG (D-HOG) (DDF) Recall Recall HOG HOG 14 TOF Recall 1 DDF HOG 0.13 D-HOG 0.09 HOG 0.17 D-HOG 0.1 HOG 0.17 D-HOG HOG D-HOG DDF 5 c 2011 Information Processing Society of Japan

図 12 手を振る動作における特徴量毎の精度比較 Fig. 12 Precision for hand-waving.

6 情報処理学会研究報告図 9 手を振る動作の姿勢推定例 Fig. 9 Examples of estimated pose for hand-waving. 図 11 走る動作の姿勢推定例 Fig. 11 Examples of estimated pose for runing. 図 12 手を振る動作における特徴量毎の精度比較 Fig. 12 Precision for hand-waving. 図 10 歩行動作の姿勢推定例 Fig. 10 Examples of estimated pose for walking. 6 c 2011 Information Processing Society of Japan

7 13 Fig. 13 Precision for walking. Fig Examples of estimated pose for each features. 5. Recall 14 Fig. 14 Precision for runing. HOG 0.07 HOG c 2011 Information Processing Society of Japan

8 1) MIRU, pp (2006). 2) HOG 3 MIRU, pp (2008). 3) 3 MIRU, pp (2010). 4) tree based filtering MIRU, pp (2006). 5) Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A. and Blake, A.: Real-Time Human Pose Recognition in Parts from Single Depth Images, CVPR (2011). 6) Luo, X., Berendsen, B., Tan, R.T. and Veltkamp, R.C.: Human Pose Estimation for Multiple Persons Based on Volume Reconstruction, ICPR (2010). 7) Baysal, S., Kurt, M.C. and Duygulu, P.: Recognizing Human Actions Using Key Poses, ICPR (2010). 8) Jiang, H.: 3D Human Pose Reconstruction Using Millions of Exemplars, ICPR (2010). 9) Deutscher, J., Blake, A. and Reid, I.: Articulated Body Motion Capture by Annealed Particle Filtering, CVPR, pp (2000). 10) Ye, L., Zhang, Q. and Guan, L.: Use Hierarchical Genetic Particle Filter to Figure Articulated Human Tracking, ICME, pp (2008). 11) Andriluka, M., Roth, S. and Schiele, B.: Pictorial structures revisited: People detection and articulated pose estimation, CVPR, pp (2009). 12) Bissacco, A., Yang, M.H. and Soatto, S.: Fast human pose estimation using appearance and motion via multi-dimensional boosting regression, CVPR, pp.1 8 (2007). 13) Ferrari, V., Marin-Jimenez, M. and Zisserman, A.: Pose search: retrieving people using their pose, CVPR, pp.1 8 (2009). 14) Xia, X., Yang, W., Li, H. and Zhang, S.: Part-based object detection using cascades of boosted classiers, ACCV, pp (2009). 15) pp (2010). 8 c 2011 Information Processing Society of Japan

A Graduation Thesis of College of Engineering, Chubu University Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata

2011 3 A Graduation Thesis of College of Engineering, Chubu University Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata CG [2] [3][4] 3 3 [1] HOG HOG TOF(Time Of Flight) iii