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Journal Articles Sensors Year : 2022

Contextual Detection of Pedestrians and Vehicles in Orthophotography by Fusion of Deep Learning Algorithms

Masoomeh Shireen Ansarnia
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Etienne Tisserand
Mohamed Amine Zidane
Yves Berviller

Abstract

In the context of smart cities, monitoring pedestrian and vehicle movements is essential to recognize abnormal events and prevent accidents. The proposed method in this work focuses on analyzing video streams captured from a vertically installed camera, and performing contextual road user detection. The final detection is based on the fusion of the outputs of three different convolutional neural networks. We are simultaneously interested in detecting road users, their motion, and their location respecting the static environment. We use YOLOv4 for object detection, FC-HarDNet for background semantic segmentation, and FlowNet 2.0 for motion detection. FC-HarDNet and YOLOv4 were retrained with our orthophotographs dataset. The last step involves a data fusion module. The presented results show that the method allows one to detect road users, identify the surfaces on which they move, quantify their apparent velocity, and estimate their actual velocity.
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Dates and versions

hal-03919940 , version 2 (03-01-2023)
hal-03919940 , version 1 (03-01-2023)

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Masoomeh Shireen Ansarnia, Etienne Tisserand, Patrick Schweitzer, Mohamed Amine Zidane, Yves Berviller. Contextual Detection of Pedestrians and Vehicles in Orthophotography by Fusion of Deep Learning Algorithms. Sensors, 2022, 22 (4), pp.1381. ⟨10.3390/s22041381⟩. ⟨hal-03919940v2⟩
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