Guo Chengyu, Ruan Songsong, Liang Xiaohui, Zhao Qinping
State Key Lab of Virtual Reality Technology and Systems, Beihang university, Xueyuan Road No.37, Haidian District, Beijing 100000, China.
Sensors (Basel). 2016 Feb 20;16(2):263. doi: 10.3390/s16020263.
Pedestrian detection and human pose estimation are instructive for reconstructing a three-dimensional scenario and for robot navigation, particularly when large amounts of vision data are captured using various data-recording techniques. Using an unrestricted capture scheme, which produces occlusions or breezing, the information describing each part of a human body and the relationship between each part or even different pedestrians must be present in a still image. Using this framework, a multi-layered, spatial, virtual, human pose reconstruction framework is presented in this study to recover any deficient information in planar images. In this framework, a hierarchical parts-based deep model is used to detect body parts by using the available restricted information in a still image and is then combined with spatial Markov random fields to re-estimate the accurate joint positions in the deep network. Then, the planar estimation results are mapped onto a virtual three-dimensional space using multiple constraints to recover any deficient spatial information. The proposed approach can be viewed as a general pre-processing method to guide the generation of continuous, three-dimensional motion data. The experiment results of this study are used to describe the effectiveness and usability of the proposed approach.
行人检测和人体姿态估计对于三维场景重建和机器人导航具有指导意义，特别是当使用各种数据记录技术捕获大量视觉数据时。使用产生遮挡或微风的无限制捕获方案，描述人体每个部分以及每个部分之间甚至不同行人之间关系的信息必须存在于静止图像中。利用这一框架，本研究提出了一种多层、空间、虚拟的人体姿态重建框架，以恢复平面图像中的任何缺失信息。在该框架中，使用基于分层部件的深度模型，利用静止图像中可用的受限信息来检测身体部位，然后与空间马尔可夫随机场相结合，以重新估计深度网络中的准确关节位置。然后，利用多个约束将平面估计结果映射到虚拟三维空间中，以恢复任何缺失的空间信息。所提出的方法可被视为一种通用的预处理方法，用于指导连续三维运动数据的生成。本研究的实验结果用于描述所提方法的有效性和实用性。
