Abstract
In this paper, we present an approach for kernel-based object tracking using the HSV color space as the feature space and fuzzy color histograms as feature vectors. These histograms are more robust to illumination changes and quantization errors than common histograms. To avoid a significant increase in the computational complexity, a simple fuzzy membership function is used. The efficiency of this approach is demonstrated using videos from the PETS database and comparing the results using the fuzzy color histogram and the common color histogram. © 2011 Springer-Verlag.
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CITATION STYLE
Villalba Espinosa, J., González Linares, J. M., Ramos Cózar, J., & Guil Mata, N. (2011). Kernel-based object tracking using a simple fuzzy color histogram. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6691 LNCS, pp. 513–519). https://doi.org/10.1007/978-3-642-21501-8_64
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