Abstract
Head detection conducted on color images has been an active research topic in the computer vision community. Recently, depth sensors have made a new type of data available, which demonstrate good invariance against illumination changes. Head detection based on depth images can be significantly simplified as background subtraction and segmentation are no longer critical issues. In this paper, a robust head detection algorithm is proposed. Firstly, a grayscale template is employed for better modeling and precise detection of human head. Meanwhile, statistical analysis of the correlation coefficients is presented and the optimal threshold is deducted. Secondly, candidate head regions are further examined by seed point selection based on a novel feature taking both correlation and local standard deviation into consideration. Finally, the detected head area is obtained by region-growing and computation efficiency issues are discussed. In order to test the validity of the proposed algorithm, we constructed a Microsoft Kinect depth database with 670 images which includes extreme conditions such as complex background and 180° rotation. Experimental results shows that the proposed algorithm achieves robust real-time head detection.
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Acknowledgement
This work was supported by the National Nature Science Foundation of China (No. 61305015, No. 61203269), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022), the National Key Research And Development Plan (No. 2016YFC0106001), and the Postdoctoral Science Foundation of China (No. 2015M580591).
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Liu, YX., Yang, Y., Li, M. (2017). Robust Real-Time Head Detection by Grayscale Template Matching Based on Depth Images. In: Huang, DS., Jo, KH., Figueroa-García, 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_60
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DOI: https://doi.org/10.1007/978-3-319-63312-1_60
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