Abstract
The paper presents an improved method for estimating the number of moving people in a scene for video surveillance applications; the performance is measured on the public database used in the framework of the PETS international competition, and compared, on the same database, with the ones participating to the same contest up to now. The system exhibits a high accuracy, ranking it at the top positions, and revealed to be so fast to make possible its use in real time surveillance applications.
This research has been partially supported by A.I.Tech s.r.l., a spin-off company of the University of Salerno (www.aitech-solutions.eu).
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Percannella, G., Vento, M. (2011). A Self-trainable System for Moving People Counting by Scene Partitioning. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_30
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DOI: https://doi.org/10.1007/978-3-642-21596-4_30
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