Real-Time Tracking with Non-Rigid Geometric Templates Using the GPU

The tracking of features in real-time video streams forms the integral part of many important applications in human-computer interaction and computer vision. Unfortunately tracking is a computationally intensive task, since the video information used by the tracker is usually prepared by applying a series of image processing filters. Thus it is difficult to realize a real-time tracker using only the CPU of a standard PC. Over the last few years, commodity graphics processing units (GPU) have evolved from fixed graphics pipeline processors into more flexible and powerful data-parallel processors. These stream processors are capable of sustaining computation rates of greater than ten times that of a single CPU. GPUs are inexpensive and are becoming ubiquitous (desktops, laptops, PDAs, cell phones). They are now capable to greatly relieve the CPU especially for large-scale parallel processing tasks, which map well to the architecture of the GPU. In this paper, we present a system, which uses a gradient vector field to track features with flexible geometric templates. Our implementation is specifically designed to suit the parallel processing architecture of the GPU. It is capable to achieve real-time performance with frame rates of around 30 frames per second