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.
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© 2006 Springer-Verlag Berlin Heidelberg
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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: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_10
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DOI: https://doi.org/10.1007/11815921_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37236-3
Online ISBN: 978-3-540-37241-7
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