GPU-Supported Object Tracking Using Adaptive Appearance Models and Particle Swarm Optimization

Boguslaw Rymut and Bogdan Kwolek: GPU-Supported Object Tracking Using Adaptive Appearance Models and Particle Swarm Optimization; Int. Conf. on Computer Vision and Graphics, 2010, Computer Vision and Graphics, Lecture Notes in Computer Science, Vol. 6375, Springer Berlin Heidelberg, 2010, pp. 227-234.

Abstract

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.

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

Bogusław Rymut and Bogdan Kwolek: GPU-Accelerated Object Tracking Using Particle Filtering and Appearance-Adaptive Models; Image Processing and Communications Challenges 2, Advances in Intelligent and Soft Computing, Vol. 84, Springer Berlin Heidelberg, 2010, pp. 337-344.

Abstract

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.

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

GPU-Accelerated Object Tracking Using Particle Filtering and Appearance-Adaptive Models; Image Processing and Communications Challenges 2, Advances in Intelligent and Soft Computing, Vol. 84, Springer Berlin Heidelberg, 2010, pp. 337-344.

Abstract

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.