Object detection is a crucial step in automating monitoring and surveillance. A classical approach to object detection employs supervised learning methods, which are effective in well-defined narrow application scopes. In this paper we propose a framework for detecting moving objects in video, which first learns autonomously and on-line the characteristic features of typical object appearances at various parts of the observed scene. The collected knowledge is then used to calibrate the system for the given scene, and to separate isolated appearances of a dominant moving object from other events. Compared to the supervised detectors, the proposed framework is self-adaptable, and therefore able to handle large diversity of objects and situations, typical for general surveillance and monitoring applications. We demonstrate the effectiveness of our framework by employing it to isolate pedestrians in public places and cars on a highway.
['Hasan Celik', 'Alan Hanjalic', 'Emile A. Hendriks']
On the development of an autonomous and self-adaptable moving object detector