An Effective 3D Human Pose Estimation Method Based on Dilated Convolutions for Videos 3D human pose estimation is a challenging problem due to the diversity of poses, human appearance, clothing, occlusion, etc. In this work, we split the problem into two stages, 2D human pose estimation and 3D pose recovery, and address it by a network based on dilated convolution for videos. We introduce pruning layer to prevent overfitting, which performs better than dropout, because the strategy of pruning is not to drop nodes randomly, but to choose the lowerweight ones. We also employ quantization to accelerate it in the smart shop environment, which gains a trade off between performance and computational complexity, and the accuracy loss is in an acceptable range. In addition, labeled human pose datasets are so limited and expensive especially for 3D poses, we propose a 3D human pose dataset named HRII in a smart shop environment, which contains more than 16k poses, 26 people and 6 scenarios of walking, hunkering, fetching objects, etc. We train and test our model on the HumanEvaI, Human3.6M and our proposed HRII, it demonstrates that the proposed method is efficient and effective.

Deep orientation: Fast and Robust Upper Body orientation Estimation for Mobile Robotic Applications An essential feature for navigating socially with a mobile robot is the upper body orientation of persons in its vicinity. For example, in a supermarket orientation indicates whether a person is looking at goods on the shelves or where a person is likely to go. However, given limited computing and battery capabilities, it is not possible to rely on highperformance graphics cards to run large, computationally expensive deep neural networks for orientation estimation in real time. Nevertheless, deep learning performs quite well for regression problems. Therefore, we tackle the problem of upper body orientation estimation with small yet efficient deep neural networks on a mobile robot in this paper. We employ a fast person detection approach as preprocessing that outputs fixed size person images before the actual estimation of the orientation is done. The combination with lightweight networks allows us to estimate a continuous angle in real time, even using a CPU only. We experimentally evaluate the performance of our system on a new, selfrecorded data set consisting of more than 100,000 RGBD samples from 37 persons, which is made publicly available. We also do an extensive comparison of different network architectures and output encodings for their applicability in estimating orientations. Furthermore, we show that depth images are more suitable for the task of orientation estimation than RGB images or the combination of both.