Learning Motion Detectors by Genetic Programming

Authors:
Brian Pinto;Andy Song
Affiliations:
School of Computer Science and Information Technology, RMIT University, Melbourne, Australia;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

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Abstract

Motion detection in videos is a challenging problem that is essential in video surveillance, traffic monitoring and robot vision systems. In this paper, we present a learning method based on Genetic Programming(GP) to evolve motion detection programs. This method eliminates the need for pre-processing of input data and minimizes the need for human expertise, which are usually critical in traditional approaches. The applicability of the GP-based method is demonstrated on different scenarios from real world environments. The evolved programs can not only locate moving objects but are also able to differentiate between interesting and uninteresting motion. Furthermore, it is able to handle variations like moving camera platforms, lighting condition changes, and cross-domain applications.