Many real-world applications require the estimation of human body joints for higher-level tasks as, for example, human behaviour understanding. In recent years, depth sensors have become a popular approach to obtain three-dimensional information. The depth maps generated by these sensors provide information that can be employed to disambiguate the poses observed in two-dimensional images. This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach. We propose a model, named Deep Depth Pose (DDP), which receives a depth map containing a person and a set of predefined 3D prototype poses and returns the 3D position of the body joints of the person. In particular, DDP is defined as a ConvNet that computes the specific weights needed to linearly combine the prototypes for the given input. We have thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which respectively depict realistic and synthetic samples, defining a new state-of-the-art on them.
One of the most popular approaches for HPE from depth maps is the one from , extended in . They use Random Forests to classify each pixel into body parts. 3D joint locations are derived from the labelled depth map by using a local mode-finding approach based on Mean Shift. Given a depth map, a point cloud is derived in @cite_11 and used as the input data for their model. Firstly, the point cloud is cleaned and transformed into a canonical coordinate frame, allowing its matching against a set of predefined exemplars. This matching provides an initial estimation that is further refined in subsequent steps. Finally, the body pose is extracted from the improved point cloud. In , random forest-based depth map labelling is combined with a probabilistic graphical model where graph-cut is applied for inference. Temporal constraints are used on sequences to improve the accuracy. In this work, we show that neither a pixel-level classification stage nor temporal information is needed to obtain the body pose.
@cite_11
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This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of pre-captured motion exemplars to generate a body configuration estimation, as well as semantic labeling of the input point cloud. The initial estimation is then refined by directly fitting the body configuration with the observation (e.g., the input depth). In addition to the new system architecture, our other contributions include modifying a point cloud smoothing technique to deal with very noisy input depth maps, a point cloud alignment and pose search algorithm that is view-independent and efficient. Experiments on a public dataset show that our approach achieves significantly higher accuracy than previous state-of-art methods. Analyzing joint movements of an athlete helps to improve the pose of the athlete. Human pose estimation (HPE) algorithms regress the locations of parts such as wrists, ankles and knees. In this paper, we propose a network that combines global and local information for HPE using a 2D image. Unlike previous works that have used global or local information separately, we use the combined information to enhance the performance of HPE. General information from a global network is used as an input to a local network to refine the location of a part using a variety of regions. The global network is based on ResNet-101 [6] and trained to regress a heatmap representing parts’ locations. The output features from the global network are used as input features for the local network. The local network learns spatial information using position sensitive score maps [11]. Through the end-to-end learning, the global network is affected by the local information. We demonstrate that the proposed HPE method is efficient on the LSP and UCF sports datasets. For applications such as autonomous driving, self-localization camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces per-pixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system. We present a random forest-based framework for real time head pose estimation from depth images and extend it to localize a set of facial features in 3D. Our algorithm takes a voting approach, where each patch extracted from the depth image can directly cast a vote for the head pose or each of the facial features. Our system proves capable of handling large rotations, partial occlusions, and the noisy depth data acquired using commercial sensors. Moreover, the algorithm works on each frame independently and achieves real time performance without resorting to parallel computations on a GPU. We present extensive experiments on publicly available, challenging datasets and present a new annotated head pose database recorded using a Microsoft Kinect.