Understanding of GP-Evolved Motion Detectors

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{Song:2013:IEEEcim,

• author = "Andy Song and Qiao Shi and Wei Yin",
• journal = "IEEE Computational Intelligence Magazine",
• title = "Understanding of GP-Evolved Motion Detectors",
• year = "2013",
• volume = "8",
• number = "1",
• month = feb,
• pages = "46--55",
• size = "7 pages",
• abstract = "Evolving solutions for machine vision applications has gained more popularity in the recent years. One area is evolving programs by Genetic Programming (GP) for motion detection, which is a fundamental component of most vision systems. Despite the good performance, this approach is not widely accepted by mainstream vision application developers. One of the reasons is that these GP generated programs are often difficult to interpret by humans. This study analyses the reasons behind the good performance and shows that the behaviours of these evolved motion detectors can be explained. Their capabilities of ignoring uninteresting motions, differentiating fast motions from slow motions, identifying genuine motions from moving background and handling noises are not random. On simplified problems we can reveal the behaviours of these programs. By understanding the evolved detectors, we can consider evolution as a good approach for creating motion detection modules.",
• keywords = "genetic algorithms, genetic programming, computer vision, image motion analysis, object detection, GP-evolved motion detector, evolution approach, machine vision application, motion detection, motion differentiation, vision system, Detectors, Human factors, Machine vision, Motion detection, Noise measurement, Videos",
• DOI = "doi:10.1109/MCI.2012.2228594",
• ISSN = "1556-603X",
• notes = "Also known as \cite{6410722}",

}

Genetic Programming entries for Andy Song Qiao Shi Wei Yin

Citations