We present an algorithm for fusing multi-viewpoint video (MVV) with inertial measurement unit (IMU) sensor data to accurately estimate 3D human pose. A 3-D convolutional neural network is used to learn a pose embedding from volumetric probabilistic visual hull data (PVH) derived from the MVV frames. We incorporate this model within a dual stream network integrating pose embeddings derived from MVV and a forward kinematic solve of the IMU data. A temporal model (LSTM) is incorporated within both streams prior to their fusion. Hybrid pose inference using these two complementary data sources is shown to resolve ambiguities within each sensor modality, yielding improved accuracy over prior methods. A further contribution of this work is a new hybrid MVV dataset (TotalCapture) comprising video, IMU and a skeletal joint ground truth derived from a commercial motion capture system.
A real-time full-body motion capture system is presented which uses input from a sparse set of inertial measurement units (IMUs) along with images from two or more standard video cameras and requires no optical markers or specialized infra-red cameras. A real-time optimization-based framework is proposed which incorporates constraints from the IMUs, cameras and a prior pose model. The combination of video and IMU data allows the full 6-DOF motion to be recovered including axial rotation of limbs and drift-free global position. The approach was tested using both indoor and outdoor captured data. The results demonstrate the effectiveness of the approach for tracking a wide range of human motion in real time in unconstrained indoor/outdoor scenes.
We propose an approach to accurately estimate 3D human pose by fusing multi-viewpoint video (MVV) with inertial measurement unit (IMU) sensor data, without optical markers, a complex hardware setup or a full body model. Uniquely we use a multi-channel 3D convolutional neural network to learn a pose embedding from visual occupancy and semantic 2D pose estimates from the MVV in a discretised volumetric probabilistic visual hull. The learnt pose stream is concurrently processed with a forward kinematic solve of the IMU data and a temporal model (LSTM) exploits the rich spatial and temporal long range dependencies among the solved joints, the two streams are then fused in a final fully connected layer. The two complementary data sources allow for ambiguities to be resolved within each sensor modality, yielding improved accuracy over prior methods. Extensive evaluation is performed with state of the art performance reported on the popular Human 3.6M dataset (Ionescu et al. in Intell IEEE Trans Pattern Anal Mach 36(7):1325-1339, 2014), the newly released TotalCapture dataset and a challenging set of outdoor videos TotalCaptureOutdoor. We release the new hybrid MVV dataset (TotalCapture) comprising of multi-viewpoint video, IMU and accurate 3D skeletal joint ground truth derived from a commercial motion capture system.
We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views. We train a symmetric convolutional autoencoder with a dual loss that enforces learning of a latent representation that encodes skeletal joint positions, and at the same time learns a deep representation of volumetric body shape. We harness the latter to up-scale input volumetric data by a factor of 4×, whilst recovering a 3D estimate of joint positions with equal or greater accuracy than the state of the art. Inference runs in real-time (25 fps) and has the potential for passive human behavior monitoring where there is a requirement for high fidelity estimation of human body shape and pose. Fig. 1. Simultaneous estimation of 3D human pose and 4× upscaled volumetric body shape, from coarse visual hull data derived from a sparse set of wide-baseline views.
Abstract. The paper presents a tool-supported approach to graphically editing scheme plans and their safety verification. The graphical tool is based on a Domain Specific Language which is used as the basis for transformation to a CSP B formal model of a scheme plan. The models produced utilise a variety of abstraction techniques that make the analysis of large scale plans feasible. The techniques are applicable to other modelling languages besides CSP B. We use the ProB tool to ensure the safety properties of collision, derailment and run-through freedom.
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