论文标题
PEDRECNET：多任务深神经网络，用于完整的3D人类姿势和方向估计
PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation
论文作者
论文摘要
我们提出了一个支持各种基于神经网络的行人检测功能的多任务网络。除了2D和3D人的姿势外，它还基于全身边界框输入支持身体和头部方向估计。这消除了对面部识别的需求。我们表明，3D人姿势估计和取向估计的性能与最新的表现相当。由于基于全身数据的3D人体姿势，特别是身体和头部方向估算的数据集很少，因此我们进一步显示了特定仿真数据的好处来训练网络。网络体系结构相对简单，但功能强大，易于适应进一步的研究和应用。
We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body bounding box input. This eliminates the need for explicit face recognition. We show that the performance of 3D human pose estimation and orientation estimation is comparable to the state-of-the-art. Since very few data sets exist for 3D human pose and in particular body and head orientation estimation based on full body data, we further show the benefit of particular simulation data to train the network. The network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.
