Depth-Based Human Detection Considering Postural Diversity and Depth Missing in Office Environment
Depth-Based Human Detection Considering Postural Diversity and Depth Missing in Office Environment
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DOI：
10.1109/access.2019.2892197
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Fujita, Kinya
中科院分区：
文献类型：
--
作者：
Fujimoto, Yuichiro;Fujita, Kinya
为了在实际的办公室工作场景中实现鲁棒的人体检测，本文提出了两种使用顶视深度相机的想法。为了处理由身体姿势引起的变化的几何人体形状（例如，坐、站和蹲），我们提出两个特征来描述人的上背部形状，即，高度连续区域的圆度和尺寸。为了减轻遮挡和红外光吸收造成的深度信息部分丢失的影响，我们提出了一种自适应的特征调整算法，该算法利用了丢失区域中隐含的信息。我们在一个有13个深度相机的系统上实现了所提出的算法。应用100小时（10个工作日）的实际办公室数据表明，上背部功能补充现有的头肩功能。它还表明，这两个建议有助于更强大的人体检测，并达到97.7%的准确率。
To realize robust human detection in an actual office work scenario, this paper proposes two ideas using top-view depth cameras. To deal with the changing geometric human shapes caused by body posture (e.g., sitting, standing, and crouching), we propose two features to describe the human upper-back shape, i.e., roundness and size of a height-continuous region. For alleviating the influences of partial loss of depth information caused by occlusions and by the absorption of infrared light, we propose an adaptive feature adjustment algorithm, which utilizes implicitly included information in the missing region. We implemented the proposed algorithm on a system with 13 depth cameras. Application to 100-hours (10 workdays) of actual office data demonstrated that the upper-back features complement the existing head-shoulder features. It also demonstrated that both of the proposals contributed to a more robust human detection and attained 97.7 % accuracy.