Abstract
Human parsing plays an important role in action understanding, clothing recommendation and human-computer interaction, etc. However, variations of human pose, clothes, viewpoint and cluttered background make the segmentation and pose estimation of body parts more difficult. In this paper, a human parsing framework is proposed based on a combination of deep skin model and part based model inference. First, a deep skin model is trained via deep belief networks, which will be used to reduce the pose searching spaces and enhance the efficiency of model inference. Secondly, pictorial structure model parses human body more accurate with the fusion maps of skin detection and HOG based part detectors. The experimental results demonstrate that the fusion of skin detection improves the detection and pose estimation of human body parts, especially for the parts such as head, arms and legs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sigal, L.: Human pose estimation. Comput. Vis. 157(10), 362–370 (2014). Springer US
Eichner, M., Marin-Jimenez, M., Zisserman, A., et al.: 2D articulated human pose estimation and retrieval in (almost) unconstrained still images. Int. J. Comput. Vis. 99(2), 190–214 (2012)
Guo, G., Lai, A.: A survey on still image based human action recognition. Pattern Recogn. 47(10), 3343–3361 (2014)
Yamaguchi, K., Kiapour, M.H., Ortiz, L.E., et al.: Retrieving similar styles to parse clothing. IEEE Trans. Pattern Anal. Mach. Intell. 37(5), 1028–1040 (2015)
Andriluka, M., Pishchulin, L., Gehler, P., et al.: 2D human pose estimation: new benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3686–3693 (2014)
Hernndez-Vela, A., Sclaroff, S., Escalera, S.: Poselet-based contextual rescoring for human pose estimation via pictorial structures. Int. J. Comput. Vis. 118(1), 49–64 (2016)
Xu, T., Wang, Y., Zhang, Z.: Pixel-wise skin colour detection based on flexible neural tree. IET Image Proc. 7(8), 751–761 (2013)
Xu, T., Zhang, Z., Wang, Y.: Patch-wise skin segmentation of human body parts via deep neural networks. J. Electron. Imaging 24(4), 043009 (2015)
Andriluka, M., Pishchulin, L., Gehler, P., et al.: MPII Human Pose Dataset (2016). http://human-pose.mpi-inf.mpg.de
Carreira, J., Agrawal, P., Fragkiadaki, K., et al.: Human pose estimation with iterative error feedback. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4733–4742 (2016)
Perez-Sala, X., Escalera, S., Angulo, C., et al.: A survey on model based approaches for 2D and 3D visual human pose recovery. Sensors 14(3), 4189–4210 (2014)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61(1), 55–79 (2005)
Sapp, B., Jordan, C., Taskar, B.: Adaptive pose priors for pictorial structures. In: IEEE Conference on Vision and Pattern Recognition (CVPR 2010), pp. 422–429. IEEE (2010)
Jones, M., Rehg, J.: Statistical color models with application to skin detection. Int. J. Comput. Vis. 46(1), 81–96 (2002)
Phung, S., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)
Toshev, A., Szegedy, C.: Deeppose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653–1660 (2014)
Tompson, J.J., Jain, A., LeCun, Y., et al.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems, pp. 1799–1807 (2014)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Pishchulin, L., Andriluka, M., Gehler, P., et al.: Strong appearance and expressive spatial models for human pose estimation. In: IEEE International Conference on Computer Vision (ICCV 2013), Sydney, Australia, pp. 3487–3494. IEEE (2013)
Johnson, S., Everingham, M.: Clustered pose and nonlinear appearance models for human poseestimation. In: British Machine Vision Conference (BMVC 2010), Aberystwyth, UK, pp. 1–11. BMVA Press (2010)
Yang, Y., Ramanan, D.: Articulated human detection with flexible mixtures of parts. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2878–2890 (2013)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 61472163, 61603151), the National Key Research & Development Plan of China (No. 2016YFB1001403), the Science and Technology Project of Shandong Province (No. 2015GGX101025), and Doctoral Foundation of University of Jinan (Nos. XBS1653, XBS1621).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xu, T., Feng, Z., Dong, L., Yang, X. (2017). 2D Human Parsing with Deep Skin Model and Part-Based Model Inference. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_70
Download citation
DOI: https://doi.org/10.1007/978-3-319-63312-1_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63311-4
Online ISBN: 978-3-319-63312-1
eBook Packages: Computer ScienceComputer Science (R0)