Real-time hierarchical visual tracking using a configurable computing machine

Abstract

This paper describes a custom computing approach to real-time visual tracking. Traditionally, tracking systems require dedicated hardware to accommodate the computational demands and input/output rates imposed by real-time video sources. A radical alternative is represented by custom computing machines such as Splash 2, which use interconnected Field-Programmable Gate Arrays (FPGAs) to provide fine-grain parallelism and reconfigurability so that high-speed performance is possible for many different applications. The efficacy of such architectures to image-based computing is illustrated here through the implementation of a tracking system that consists of two parts: a Gaussian pyramid generator and a correlation-based tracker. The pyramid generator converts each input image to a hierarchy of images, each representing the original image at a different resolution. An object is tracked on successive frames by a coarse-to-fine search through this image hierarchy, using the sum of absolute differences as the matching criterion. Splash 2 performs these operations at rates of 15 or 30 frames per second. Its performance therefore rivals that of application-specific systems, although the architecture is inherently general-purpose in nature.