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Suivi de passagers de bus par apprentissage profond

Abstract : In this paper we present a comparative study of tracking-by-detection approaches applied to passenger counting in city buses. A detector targets passengers at each frame, a tracker then matches detections together through time to produce trajectories. We compare three deep learning detectors still under-explored in our context, and couple them with a real time tracker for global evaluation on our large scale in situ dataset. The results we present are very encouraging in terms of detection, tracking rate and speed expected for our embedded perspectives.
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Contributor : Claire Labit-Bonis <>
Submitted on : Thursday, July 5, 2018 - 1:43:59 PM
Last modification on : Thursday, June 10, 2021 - 3:02:47 AM
Long-term archiving on: : Monday, October 1, 2018 - 4:57:29 PM


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  • HAL Id : hal-01830834, version 1


Claire Labit-Bonis, Jérôme Thomas, Frédéric Lerasle, Jorge Francisco Madrigal Diaz. Suivi de passagers de bus par apprentissage profond. Congrès Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP 2018), Jun 2018, Marne-la-Vallée, France. 8p. ⟨hal-01830834⟩



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