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Fast Tracking-by-Detection of Bus Passengers with Siamese CNNs

Abstract : Knowing the exact number of passengers among the city bus fleets allows public transport operators to optimally distribute their vehicles into the traffic. However, interpreting overcrowded scenarios, at rush hour, with day/night illumination changes can be tricky. Based on the visual tracking-by-detection paradigm, we benefit from video stream information provided by cameras placed above doors to infer people trajectories and deduce the number of enter-ings/leavings at every bus stop. In this way a person detector estimates the location of the passengers in each image, a tracker matches detections between successive frames based on different cues such as appearance or motion, and infers trajectories over time. This paper proposes a fast and embeddable framework that performs person detection using relevant state-of-the-art CNN detectors, and couple the best one (in our applicative context) with a newly designed Siamese network for real-time tracking/data association purposes. Evaluations on our own large scale in-situ dataset are very promising in terms of performances and real-time constraint expected for on-board processing.
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Contributor : Claire Labit-Bonis <>
Submitted on : Tuesday, December 10, 2019 - 11:40:56 AM
Last modification on : Thursday, June 10, 2021 - 3:05:44 AM
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Claire Labit-Bonis, Jérôme Thomas, Frédéric Lerasle, Jorge Francisco Madrigal Diaz. Fast Tracking-by-Detection of Bus Passengers with Siamese CNNs. 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2019), Sep 2019, Taipei, Taiwan. pp.1-8, ⟨10.1109/AVSS.2019.8909843⟩. ⟨hal-02401951⟩



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