Abstract
In this work we introduce SAFaD: A System for Automatic Fall Detection on Surveillance Imagery. Our system heavily relies in an intermediate representation that allows us to accurately and optimally encode the motion of a person. An ensemble of different approaches use this volume as input to predict whether a fall event has happened. We tested out system with a state-of-the-art fall detection dataset reaching a 62.86% accuracy.
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This work has been funded by the Spanish Government PID2019-104818RB-I00 grant, supported with Feder funds.
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Perez-Lopez, B., Gomez-Donoso, F., Cazorla, M. (2023). SAFaD: A System for Automatic Fall Detection on Surveillance Imagery. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-031-21062-4_46
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DOI: https://doi.org/10.1007/978-3-031-21062-4_46
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