A new, computationally efficient windowing methodology for motion tracking is described. The proposed approach is well suited to real-time focus-of-attention applications in which regions-of-interest, or windows, are used to reduce image data rates. Applications include robot guidance, where high speed image processing is required for real-time position control in operations such as fixtureless assembly for flexible manufacturing.

A hierarchy of windowing functions which includes motion detection and target detection and tracking has been developed. This has resulted in a new algorithm for corner detection in image windows, as well as a proposal for measuring the information content of an image based on corner location accuracy. The techniques have been experimentally verified with the implementation of a vision system based on a high speed digital camera, a custom-built video interface board, and a network of digital signal processors. Dynamically positioned at video frame rates, windows within the camera field-of-view are made cooperative by exchanging information among their corresponding processors to allow real-time adaptation to visual motion. A cooperative windowing scheme using two networked target tracking windows is demonstrated. Motion tracking is based on the best-case output of the simultaneous application of a feature-based algorithm applied in the first window and a model-based algorithm running in the second. The experimental results demonstrate the advantages of motion tracking using this cooperative windows approach.