Computer vision is one of the key research topics of modern computer science and finds application in manufacturing, surveillance, automotive, robotics, and sophisticated human-machine-interfaces. These applications require small and efficient solutions which are commonly provided as embedded systems. This means that there exist resource constraints, but also the need for increasing adaptivity and robustness. This paper proposes an autonomic computing framework for robust object tracking. A probabilistic tracking algorithm is combined with the use of multi-filter fusion of redundant image filters. The system can react on unpredictable changes in the environment through self-adaptation. Due to resource constraints, the number of filters actively used for tracking is limited. By means of self-organization, the system structure is re-organized to activate filters adequate for the current context. The proposed framework is designed for, but not limited to, embedded computer vision. Experimental evaluations demonstrate the benefit of the approach.
['Stefan Wildermann', 'Andreas Oetken', 'Jürgen Teich', 'Zoran Salcic']
Self-organizing computer vision for robust object tracking in smart cameras