Kinect-supported dataset creation for human pose estimation
Kinect-supported dataset creation for human pose estimation
复制标题
Kinect 支持的人体姿势估计数据集创建
DOI：
10.1145/2643188.2643195
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
A. Páldy
中科院分区：
文献类型：
--
作者：
Kamil Behún;A. Herout;A. Páldy
很难找到用于人体姿势估计的特定任务的训练和评估数据集。本文提出了一种快速构建精确注释的训练数据集的方法，用于人体坐姿估计，特别是用于航空驾驶舱。我们建议使用Kinect作为一种工具，用于将地面实况收集到纯视觉数据集（由于应用程序定义的原因，使用Kinect或类似的基于结构光的方法是不可能的）。由于个别关节的Kinect注释可能是不精确的，在某些时刻，所获得的数据的手动后处理是必要的，我们提出了一个高效和可靠的手动后注释方案。我们制作了一个包含6，322个注释帧的数据集，涉及11个在各种照明条件，不同服装和不同背景下记录的人类主体。每个帧包含一个坐着的人在正面视图与姿势和光流数据的注释。我们在生成的数据集上使用基于随机森林的身体部位检测器来验证其可用性。这些初步结果表明，检测器可以成功地训练开发的数据集和光流有助于检测精度显着。数据集及其创建过程中使用的中间数据是公开可用的。通过这一点，我们打算支持进一步的研究和评估的特定主题的人体姿态估计集中在一个坐在一个驾驶舱的情况下。
Training and evaluation datasets for specific tasks of human pose estimation are hard to find. This paper presents an approach for rapid construction of a precisely annotated training dataset for human pose estimation of a sitting subject, intended especially for aeronautic cockpit. We propose to use Kinect as a tool for collecting ground truth to a purely visual dataset (for reasons defined by the application, use of Kinect or similar structured light-based approaches is impossible). Since Kinect annotation of individual joints might be imprecise at certain moments, manual post-processing of the acquired data is necessary and we propose a scheme for efficient and reliable manual post-annotation. We produced a dataset of 6,322 annotated frames, involving 11 human subjects recorded in various lighting conditions, different clothing, and varying background. Each frame contains one seated person in frontal view with annotation of pose and optical flow data. We used detectors of body parts based on Random Forest on the produced dataset in order to verify its usability. These preliminary results show that the detector can be trained successfully on the developed dataset and that the optical flow contributes to the detection accuracy considerably. The dataset and the intermediary data used during its creation is made publicly available. By this, we intend to support further research and evaluation in the specific topic of human pose estimation focused on a sitting subject in a cockpit scenario.
DOI：
10.1023/b:visi.0000042934.15159.49
发表时间：
2005-01-01
影响因子：
19.5
作者：
Felzenszwalb, PF;Huttenlocher, DP
通讯作者：
Huttenlocher, DP