Online tracking of multiple objects using WiSARD

Abstract : This paper evaluates the WiSARD weightless model as a classification system on the problem of tracking multiple objects in real- time. Exploring the structure of this model, the proposed solution applies a re-learning stage in order to avoid interferences caused by background noise or variations in the target shape. Once the tracker finds a target at the first time, it applies only local searches around the neighborhood in order to have fast response. This approach is evaluated through some experiments on real-world video data.
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Communication dans un congrès
ESANN 2014, 22st European Symposium on Artificial Neural Networks, Computational Intelligence And Machine Learning, Apr 2014, Bruges, Belgium. pp 541-546, 2014
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Rafael Lima de Carvalho, Danilo S. C. Carvalho, Felix Antonio Claudio Mora-Camino, Priscila V. M. Lima, Felipe M. G. França. Online tracking of multiple objects using WiSARD. ESANN 2014, 22st European Symposium on Artificial Neural Networks, Computational Intelligence And Machine Learning, Apr 2014, Bruges, Belgium. pp 541-546, 2014. 〈hal-01059678〉

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