Implementations of a feature-based visual tracking algorithm on two MIMD machines

As an example of a task that processes complex visual information to generate control signals for a system, an existing feature-based visual tracking algorithm for a static camera was mapped onto two parallel machines representing the MIMD execution model. The algorithm is described and a version suitable for mapping onto parallel machines is developed. Timing results for the implementation on the Intel Paragon and the IBM SP2 are presented, using real image data for all experiments. For each subtask of the algorithm, its performance is measured as a function of data layout. In addition, the impact of the time required to distribute image data across processing elements on the performance is considered. For the subtask of finding the best match of a feature in an image, load balancing approaches dependent on machine characteristics and submachine size are discussed. This type of matching is used in many vision tasks