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Learning rich features from objectness estimation for human lying-pose detection

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Abstract

Lying-pose human detection is an active research field of computer vision in recent years. It has a good theoretical significance and furthermore many applications, such as victim detection or home service robot. But the study on lying-pose human detection in low-altitude overlooking images have many unsolved problems owing to multiple poses, arbitrary orientation, in-plane rotation, perspective distortion, and time-consuming. In this paper, the proposed framework of human lying-pose detection is optimization and machine learning algorithms inspired by processes of neurobiology suggest and human vision system to select possible object locations. First, the proposed model effectively utilizes binarized normed gradient features to obtain the objectness rapidly based on the vision saliency. Further, deep-learning techniques based on the convolution neural network are trained for learning rich feature hierarchies, in order to obtain the object of lying-pose human from objectness estimation, unlike the classical sliding-window algorithm. Eventually, employed pyramid mean-shift algorithm and rotation-angle recovery method to find position and direction of human lying-pose. The experimental results show that our method is rapid and efficient, and that it achieves state-of-the-art results with our XMULP dataset.

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Notes

  1. http://www.sucro.org/homepage/wanghaibo/SDUFall.html.

  2. https://sites.google.com/site/kinectfalldetection/.

  3. http://sites.google.com/site/occlusiondataset.

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Acknowledgments

This work was supported by the Nature Science Foundation of China (No. 61202143), the Collaborative Innovation Special Foundation of Xuchang University (No. XCUXT2014-08), and the Natural Science Foundation of Guizhou Province (No. QKHLHZi [2015] 7784).

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Authors and Affiliations

  1. School of Information Science and Technology, Xiamen University, Xiamen, 361005, Fujian, China

    Dao-Xun Xia, Song-Zhi Su & Shao-Zi Li

  2. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, 550001, Guizhou, China

    Dao-Xun Xia

  3. School of Urban Planning and Landscaping, Xuchang University, Xuchang, 461000, Hebei, China

    Li-Chuan Geng

  4. The UAVLARS Collaborative Innovation Center, Xuchang University, Xuchang, 461000, Hebei, China

    Guo-Xi Wu

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  1. Dao-Xun Xia
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Correspondence to Shao-Zi Li.

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Communicated by S. Kopf.

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Xia, DX., Su, SZ., Geng, LC. et al. Learning rich features from objectness estimation for human lying-pose detection. Multimedia Systems 23, 515–526 (2017). https://doi.org/10.1007/s00530-016-0518-5

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