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

Abstract

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.