Abstract
A Growing Competitive Neural Network system is presented 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. © 2009 Springer Berlin Heidelberg.
Cite
CITATION STYLE
Luque, R. M., Ortiz-De-Lazcano-Lobato, J. M., Lopez-Rubio, E., & Palomo, E. J. (2009). Object tracking in video sequences by unsupervised learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5702 LNCS, pp. 1070–1077). https://doi.org/10.1007/978-3-642-03767-2_130
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