Comparative study of people detection in surveillance scenes

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Abstract

We address the problem of determining if a given image region contains people or not, when environmental conditions such as viewpoint, illumination and distance of people from the camera are changing. We develop three generic approaches to discriminate between visual classes: ridge-based structural models, ridge-normalized gradient histograms, and linear auto-associative memories. We then compare the performance of these approaches on the problem of people detection for 26 video sequences taken from the CAVIAR database. © Springer-Verlag Berlin Heidelberg 2006.

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Negre, A., Tran, H., Gourier, N., Hall, D., Lux, A., & Crowley, J. L. (2006). Comparative study of people detection in surveillance scenes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4109 LNCS, pp. 100–108). Springer Verlag. https://doi.org/10.1007/11815921_10

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