Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Kernel Particle Filter for Real-Time 3D Body Tracking in Monocular Color Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Segmentation and Tracking of Multiple Humans in Crowded Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Swarm intelligence based searching schemes for articulated 3D body motion tracking
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Full body motion tracking in monocular images using particle swarm optimization
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Particle Swarm Optimization and Differential Evolution for model-based object detection
Applied Soft Computing
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This paper presents methods that utilize the advantages of modern graphics card hardware for real-time full body tracking with a 3D body model. By means of the presented methods the tracking of full body can be performed at frame-rates of 5 frames per second using a single low-cost moderately-priced graphics card and images from single camera. For a model with 26 DOF we achieved 15 times speed-up. The pose configuration is given by the position and orientation of the pelvis as well as relative joint angles between the connected limbs. The tracking is done through searching for a model configuration that best corresponds to the observed human silhouette in the input image. The searching is done via particle swarm optimization, where each particle corresponds to some hypothesized set of model parameters.