SCOOP: A Real-Time Sparsity Driven People Localization Algorithm

Abstract : Detecting and tracking people in scenes monitored by cameras is an important step in many application scenarios such as surveillance, urban planning or behavioral studies to name a few. The amount of data produced by camera feeds is so large that it is also vital that these steps be performed with the utmost computational efficiency and often even real-time. We propose SCOOP, a novel algorithm that reliably detects pedestrians in camera feeds, using only the output of a simple background removal technique. SCOOP can handle a single or many video feeds. At the heart of our technique there is a sparse model for binary motion detection maps that we solve with a novel greedy algorithm based on set covering. We study the convergence and performance of the algorithm under various degradation models such as noisy observations and crowded environments, and we provide mathematical and experimental evidence of both its efficiency and robustness using standard datasets. This clearly shows that SCOOP is a viable alternative to existing state-of-the-art people detection algorithms, with the marked advantage of real-time computations.
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Journal of Mathematical Imaging and Vision, Springer Verlag, 2014, 48 (1), pp.160-175. 〈10.1007/s10851-012-0405-4〉
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Contributeur : Jules Espiau de Lamaestre <>
Soumis le : vendredi 8 juin 2012 - 16:10:09
Dernière modification le : lundi 2 octobre 2017 - 16:06:02

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Mohammad Golbabaee, Alexandre Alahi, Pierre Vandergheynst. SCOOP: A Real-Time Sparsity Driven People Localization Algorithm. Journal of Mathematical Imaging and Vision, Springer Verlag, 2014, 48 (1), pp.160-175. 〈10.1007/s10851-012-0405-4〉. 〈hal-00705992〉

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