Abstract
We extract local empirical templates and density ratios from a large collection of surveillance videos, and develop a fast and low-cost scheme for people counting. The local empirical templates are extracted by clustering the foregrounds induced by single pedestrians with similar features in silhouettes. The density ratio is obtained by comparing the size of the foreground induced by a group of pedestrians to that of the local empirical template considered the most appropriate for the region where the group foreground is captured. Because of the local scale normalization between sizes, the density ratio appears to have a bound closely related to the number of pedestrians that induce the group foreground. We estimate the bounds of density ratios for groups of different numbers of pedestrians in the learning phase, and use the estimated bounds to count the pedestrians in online settings. The results are promising.
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Hung, D.H., Chung, SL., Hsu, GS. (2011). Local Empirical Templates and Density Ratios for People Counting. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_8
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DOI: https://doi.org/10.1007/978-3-642-19282-1_8
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