Abstract
This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance.
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Marana, A.N., Cavenaghi, M.A., Ulson, R.S., Drumond, F.L. (2005). Real-Time Crowd Density Estimation Using Images. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds) Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595755_43
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DOI: https://doi.org/10.1007/11595755_43
Publisher Name: Springer, Berlin, Heidelberg
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