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.
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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).
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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
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