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.