Boguslaw Rymut

GPU-supported object tracking using adaptive appearance models and particle swarm optimization

This paper demonstrates how CUDA-capable Graphics Processor Unit can be effectively used to accelerate a tracking algorithm based on adaptive appearance models. The object tracking is achieved by particle swarm optimization algorithm. Experimental results show that the GPU implementation of the algorithm exhibits a more than 40-fold speed-up over the CPU implementation.

Real-Time Multiview Human Body Tracking Using GPU-Accelerated PSO

This paper presents our approach to 3D model-based human motion tracking using a GPU-accelerated particle swarm optimization. The tracking involves configuring the 3D human model in the pose described by each particle and then rasterizing it in each particle’s 2D plane. In our implementation, we launch one independent thread for each column of each 2D plane. Such a parallel algorithm exhibits the level of parallelism that allows us to effectively utilize the GPU resources. Owing to such task decomposition the tracking of the full human body can be performed at rates of 15 frames per second. The GPU achieves an average speedup of 7.5 over the CPU. The speedup that achieves the GPU over CPU grows with the number of the particles. For marker-less motion capture system consisting of four calibrated and synchronized cameras, the efficiency comparisons were conducted on four CPU cores and four GTX GPUs on two cards.

Real-time multiview human pose tracking using graphics processing unit-accelerated particle swarm optimization

This paper describes how to achieve real‐time tracking of 3D human motion using multiview images and graphics processing unit (GPU)‐accelerated particle swarm optimization. The tracking involves configuring the 3D human model in the pose described by each particle and then rasterizing it in each 2D plane. The Compute Unified Device Architecture threads rasterize the columns of the triangles and perform the summing of the fitness values of pixels belonging to the processed columns. Such a parallel particle swarm optimization (PSO) exhibits the level of parallelism that allows us to effectively utilize the GPU resources. Image acquisition and image processing are multithreaded and run on CPU in parallel with PSO‐based searching for the best pose. Owing to such task decomposition, the tracking of the full human body can be performed at rates of 12 frames per second. For a PSO consisting of 1000 particles and executing 10 iterations, the GPU achieves an average speedup of 12 over the CPU. Using marker‐less motion capture system consisting of four calibrated and synchronized cameras, the efficiency comparisons were conducted on four CPU cores and four GTX GPUs on two cards. Copyright

GPU-Accelerated Human Motion Tracking Using Particle Filter Combined with PSO

This paper discusses how to combine particle filter (PF) with particle swarm optimization (PSO) to achieve better object tracking. Owing to multi-swarm based mode seeking the algorithm is capable of maintaining multimodal probability distributions and the tracking accuracy is far better than accuracy of PF or PSO. We propose parallel resampling scheme for particle filtering running on GPU. We show the efficiency of the parallel PF-PSO algorithm on 3D model based human motion tracking. The 3D model is rasterized in parallel and single thread processes one column of the image. Such level of parallelism allows us to efficiently utilize the GPU resources and to perform tracking of the full human body at rates of 15 frames per second. The GPU achieves an average speedup of 7.5 over the CPU. For marker-less motion capture system consisting of four calibrated cameras, the computations were conducted on four CPU cores and four GTX GPUs on two cards.

Marker-Less 3D Human Motion Capture in Real-Time Using Particle Swarm Optimization with GPU-Accelerated Fitness Function

In model-based 3D motion tracking the most computationally demanding operation is evaluation of the objective function, which expresses similarity between the projected 3D model and image observations. In this work, marker-less tracking of full body has been realized in a multi-camera system using Particle Swarm Optimization. In order to accelerate the calculation of the fitness function the rendering of the 3D model in the requested poses has been realized using OpenGL. The experimental results show that the calculation of the fitness score with CUDA-OpenGL is up to 40 times faster in comparison to calculation it on a multi-core CPU using OpenGL-based model rendering. Thanks to CUDA-OpenGL acceleration of calculation of the fitness function the reconstruction of the full body motion can be achieved in real-time.

GPU-Accelerated Object Tracking Using Particle Filtering and Appearance-Adaptive Models

In this work we present an object tracking algorithm running on GPU. The tracking is achieved by a particle filter using appearance-adaptive models. The main focus of our work is parallel computation of the particle weights. The tracker yields promising GPU/CPU speed-up. We demonstrate that the GPU implementation of the algorithm that runs with 256 particles is about 30 times faster than the CPU implementation. Practical implementation issues in the CUDA framework are discussed. The algorithm has been tested on freely available test sequences.

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.

Mixing Graphics and Compute for Real-Time Multiview Human Body Tracking

This paper presents an effective algorithm for 3D model-based human motion tracking using a GPU-accelerated particle swarm optimization. The tracking involves configuring the 3D human model in the pose described by each particle and then rasterizing it in each camera view. In order to accelerate the calculation of the fitness function, which is the most computationally demanding operation of the algorithm, the rendering of the 3D model has been realized using CUDA-OpenGL interoperability. Since CUDA and OpenGL both run on GPU and share data through common memory the CUDA-OpenGL interoperability is very fast. We demonstrate that thanks to GPU hardware rendering the time needed for calculation of the objective function is shorter. Owing to more precise rendering of the 3D model as well as better extraction of its edges the human motion tracing is more accurate.

Full body motion tracking in monocular images using particle swarm optimization

The estimation of full body pose in monocular images is a very difficult problem. In 3D-model based motion tracking the challenges arise as at least one-third of degrees of freedom of the human pose that needs to be recovered is nearly unobservable in any given monocular image. In this paper, we deal with high dimensionality of the search space through estimating the pose in a hierarchical manner using Particle Swarm Optimization. Our method fits the projected body parts of an articulated model to detected body parts at color images with support of edge distance transform. The algorithm was evaluated quantitatively through the use of the motion capture data as ground truth.

Real-Time tracking of full-body motion using parallel particle swarm optimization with a pool of best particles

In this paper we present a particle swarm optimization (PSO) based approach for marker-less full body motion tracking. The objective function is smoothed in an annealing scheme and then quantized. This allows us to extract a pool of candidate best particles. The algorithm selects a global best from such a pool to force the PSO jump out of stagnation. Experiments on 4-camera datasets demonstrate the robustness and accuracy of our method. The tracking is conducted on 2 PC nodes with multi-core CPUs, connected by 1 GigE. This makes our system capable of accurately recovering full body movements with 14 fps.