Real-time people counting using blob descriptor

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Abstract

We propose a system for counting the number of pedestrians in real-time. This system estimates "how many pedestrians are and where they are in video sequences" by the following procedures. First, candidate regions are segmented into blobs according to background subtraction. Second, a set of features are extracted from each blob and a neural network estimates the number of pedestrians corresponding to each set of features. To realize real-time processing, we used only simple and valid features, and the adaptive background modeling using Parzen density estimation, which realizes fast and accurate object detection in input images. We also validate the effectiveness of the proposed system by several experiments.

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Yoshinaga, S., Shimada, A., & Taniguchi, R. I. (2010). Real-time people counting using blob descriptor. In Procedia - Social and Behavioral Sciences (Vol. 2, pp. 143–152). https://doi.org/10.1016/j.sbspro.2010.01.028

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