Background compensation and an active-camera motion tracking algorithm

Motion tracking using an active camera is a very computationally complex problem. Existing serial algorithms have provided frame rates that are much lower than those desired, mainly because of the lack of computational resources. Parallel computers are well suited to image processing tasks and can provide the computational power that is required for real-time motion tracking algorithms. This paper develops a parallel implementation of a known serial motion tracking algorithm, with the goal of achieving greater than real-time frame rates, and to study the effects of data layout, choice of parallel mode of execution, and machine size on the execution time of this algorithm. A distinguishing feature of this application study is that the portion of each image frame that is relevant changes from one frame to the next based on the camera motion. This impacts the effect of the chosen data layout on the needed inter-processor data transfers and the way in which work is distributed among the processors. Experiments were performed to determine for which image sizes and number of processors which data layout would perform better. The parallel computers used in this study are the MasPar MP-1, Intel Paragon, and PASM. Different modes are examined and it is determined that mixed mode is faster than SIMD or MIMD implementations.