High performance memetic algorithm particle filter for multiple object tracking on modern GPUs

This work presents an effective approach to visual tracking using a graphics processing unit (GPU) for computation purposes. In order to get a performance improvement against other platforms it is convenient to select proper algorithms such as population-based ones. They expose a parallel-friendly nature needing from many independent evaluations that map well to the parallel architecture of the GPU. To this end we propose a particle filter (PF) hybridized with a memetic algorithm (MA) to produce a MAPF tracking algorithm for single and multiple object tracking problems. Previous experimental results demonstrated that the MAPF algorithm showed more accurate tracking results than the standard PF, and now we extend those results with the first complete adaptation of the PF and the MAPF for visual tracking to the NVIDIA CUDA architecture. Results show a GPU speedup between 5x-16x for different configurations.