Abstract
This paper describes a method for estimating human distributions (quantities and locations) based on multiple-viewpoint image sequences. In the field of human image analysis, inter-human occlusion is a significant problem: when a scene includes a large number of occlusions, tracking of individual persons becomes difficult. Therefore, updating a tracking-based model is not enough to estimate the distribution in complex scenes. In our method, the number of persons and their locations are directly estimated from a set of input images based on the fitting of a projected shape model. The model’s complexity (number of persons) is determined based on the MDL (minimum description length) criterion. In addition, the image areas occluded by static objects are also detected and automatically excluded from the human distribution computations. We confirmed the feasibility of the proposed method through experiments using both synthesized and real images. Results show the effectiveness of our method.
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Utsumi, A., Yamazoe, H., Hosaka, Ki., Igi, S. (2006). Human Distribution Estimation Using Shape Projection Model Based on Multiple-Viewpoint Observations. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_80
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DOI: https://doi.org/10.1007/11612032_80
Publisher Name: Springer, Berlin, Heidelberg
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