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.


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.