Abstract
The aim of this paper is to present the use of Growing Competitive Neural Networks as a precise method to track moving objects for video-surveillance. The number of neurons in this neural model can be automatically increased or decreased in order to get a one-to-one association between objects currently in the scene and neurons. This association is kept in each frame, what constitutes the foundations of this tracking system. Experiments show that our method is capable to accurately track objects in real-world video sequences.
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Ortiz-de-Lazcano-Lobato, J.M., Luque, R.M., López-Rodríguez, D., Palomo, E.J. (2009). Growing Competitive Network for Tracking Objects in Video Sequences. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_12
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DOI: https://doi.org/10.1007/978-3-642-04921-7_12
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