A Novel 3D Head Multi-feature Constraint Method for Human Localization Based on Multiple Depth Cameras
文献类型：会议论文
| 作者 | Feixiang Zhou; Haikuan Wang; Zhile Yang; Dong Xie |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 重庆 |
| 英文摘要 | Up to date, the majority of existing spatial localization methods is based on visual positioning methods and non-visual positioning methods. In the vision-based positioning method, the traditional 2D human detection method are vulnerable to tackle with environmental changes including illumination, complex background, object occlusion, shadow interference and other factors, due to which the algorithm is less robust and difficult to achieve accurate target positioning. In respects to 3D human positioning, binocular vision or multivision approaches have been widely used to acquire depth information. The complexity of the algorithm is high, and the detection range is limited. To deal with this, a spatial location method based on multiple depth cameras is proposed in this paper. Multiple 3D-TOF depth cameras are jointly used to directly obtain depth information. Histograms of Oriented Depth (HOD) features are then extracted and trained to find the human head and shoulder region. Moreover, Spatial Density of Head (SDH) and the Convexity and Square Similarity of Head (CSSH) features are combined to determine the human target. Finally, the positioning data of multiple cameras are determined by using Nearest Center Point (NCP) to obtain the final human body positioning information. Experimental results show that the proposed method can not only obtain higher recognition rate and positioning accuracy, but also enable a larger detection range, meeting the needs of large-scale spatial positioning. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14077]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Feixiang Zhou,Haikuan Wang,Zhile Yang,et al. A Novel 3D Head Multi-feature Constraint Method for Human Localization Based on Multiple Depth Cameras[C]. 见:. 重庆. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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