3D Reconstruction Of Human Motion Research Articles

Reconstruction of 3D human motion in real-time using particle swarm optimization with GPU-accelerated fitness function

In this paper, a novel framework for acceleration of 3D model-based, markerless visual tracking in multi-camera videos is proposed. The objective function being the most computationally demanding part of model-based 3D motion reconstruction is calculated on a GPU. The proposed framework effectively utilizes the rendering power of OpenGL to render the 3D models in the predicted poses, whereas the CUDA threads are used to match such rendered models with the image observations and to perform particle swarm optimization-based tracking. We demonstrate effective parallelization of the particle swarm optimization on GPU. Execution of time-consuming parts of the algorithm on GPU using CUDA-OpenGL significantly accelerates the 3D motion reconstruction, making our method capable of tracking full-body movements with a maximum speed of 15 fps. Qualitative and quantitative experimental results on various four-camera benchmark datasets demonstrate the efficiency and accuracy of our method for real-time motion tracking.

Automatic reconstruction of 3D human motion pose from uncalibrated monocular video sequences based on markerless human motion tracking

We present a method to reconstruct human motion pose from uncalibrated monocular video sequences based on the morphing appearance model matching. The human pose estimation is made by integrated human joint tracking with pose reconstruction in depth-first order. Firstly, the Euler angles of joint are estimated by inverse kinematics based on human skeleton constrain. Then, the coordinates of pixels in the body segments in the scene are determined by forward kinematics, by projecting these pixels in the scene onto the image plane under the assumption of perspective projection to obtain the region of morphing appearance model in the image. Finally, the human motion pose can be reconstructed by histogram matching. The experimental results show that this method can obtain favorable reconstruction results on a number of complex human motion sequences.

BM³E : Discriminative Density Propagation for Visual Tracking

We introduce BM3 E, a Conditional Bayesian Mixture of Experts Markov Model, for consistent probabilistic estimates in discriminative visual tracking. The model applies to problems of temporal and uncertain inference and represents the unexplored bottom-up counterpart of pervasive generative models estimated with Kalman filtering or particle filtering. Instead of inverting a non-linear generative observation model at run-time, we learn to cooperatively predict complex state distributions directly from descriptors that encode image observations - typically bag-of-feature global image histograms or descriptors computed over regular spatial grids. These are integrated in a conditional graphical model in order to enforce temporal smoothness constraints and allow a principled management of uncertainty. The algorithms combine sparsity, mixture modeling, and non-linear dimensionality reduction for efficient computation in high-dimensional continuous state spaces. The combined system automatically self-initializes and recovers from failure. The research has three contributions: (1) We establish the density propagation rules for discriminative inference in continuous, temporal chain models; (2) We propose flexible supervised and unsupervised algorithms for learning feedforward, multivalued contextual mappings (multimodal state distributions) based on compact, conditional Bayesian mixture of experts models; (3) We validate the framework empirically for the reconstruction of 3d human motion in monocular video sequences. Our tests on both real and motion capture-based sequences show significant performance gains with respect to competing nearest-neighbor, regression, and structured prediction methods.