标题：Robust real-time head detection by grayscale template matching based on depth images
作者：Liu, Yun-Xia ;Yang, Yang ;Li, Min
作者机构：[Liu, Yun-Xia ] School of Information Science and Engineering, University of Jinan, Jinan, China;[Yang, Yang ;Li, Min ] School of Information Science 更多
会议名称：13th International Conference on Intelligent Computing, ICIC 2017
会议日期：7 August 2017 through 10 August 2017
来源：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
关键词：Correlation analysis; Depth image; Head detection; Template matching
摘要：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. © Springer International Publishing AG 2017.