The ability to stabilize the image of one moving object in the presence of others by active movements of the visual sensor is an essential task for biological systems, as well as for autonomous mobile robots. An algorithm is presented that evaluates the necessary movements from acquired visual data and controls an active camera system (ACS) in a feedback loop. No a priori assumptions about the visual scene and objects are needed. The algorithm is based on functional models of human pursuit eye movements and is to a large extent influenced by structural principles of neural information processing. An intrinsic object definition based on the homogeneity of the optical flow field of relevant objects, i.e., moving mainly fronto- parallel, is used. Velocity and spatial information are processed in separate pathways, resulting in either smooth or saccadic sensor movements. The program generates a dynamic shape model of the moving object and focuses its attention to regions where the object is expected. The system proved to behave in a stable manner under real-time conditions in complex natural environments and manages general object motion. In addition it exhibits several interesting abilities well-known from psychophysics like: catch-up saccades, grouping due to coherent motion, and optokinetic nystagmus.

Date Published: 1 November 1992
PDF: 14 pages
Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131564

Published in SPIE Proceedings Vol. 1825:
Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)