Abstract
The aim of our project is to design an algorithm for counting people in public transport vehicles such as buses by processing images from surveillance cameras’ video streams. This article presents a method of detection and tracking of multiple faces in a video by using a model of first and second order local moments. The three essential steps of our system are skin color modeling, probabilistic shape modeling and bayesian detection and tracking. An iterative process is used to estimate the position and shape of multiple faces in images, and to track them in video streams.
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© 2005 Springer-Verlag Berlin Heidelberg
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Harasse, S., Bonnaud, L., Desvignes, M. (2005). Finding People in Video Streams by Statistical Modeling. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_67
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DOI: https://doi.org/10.1007/11552499_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
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