PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation
This work addresses a domain-specific problem for computer vision applications like surveillance or robotics, but it is incremental as it builds on existing multitask networks with a focus on simulation data.
The authors tackled the problem of estimating full 3D human pose and orientation from full body bounding boxes, eliminating the need for face recognition, and achieved performance comparable to state-of-the-art methods.
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.