Novel Convolutional Neural Network-Based Roadside Unit for Accurate Pedestrian Localisation

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Abstract

Hazardous situations may easily be caused by limited visibility at urban traffic intersections due to buildings, fences, flora, and other obstacles. Thus, drivers approaching an intersection have limited reaction time when other obscured road users, such as pedestrians and cyclists, appear unexpectedly. Previous research has been conducted on applications warning drivers of approaching out-of-sight vehicles. However, less focus has been on the detection and awareness applications revealing the presence of pedestrians. We propose a novel system that displays the driver real-time locations and types of hidden road users at traffic intersections. A roadside unit is installed in the infrastructure which sends safety-critical object data to the vehicle, supporting the real-time decision-making of the driver. The roadside unit consists of a monovision camera streaming video to a computing unit which performs object detection and distance measurements on the detected objects. This paper validates the capability of the proposed system of localizing a pedestrian, and also examines its sensitivity to installation and detection errors. The results show that the accuracy of the proposed system is suitable for the intended application. However, an error in the vertical angle of the roadside unit camera caused an exponential error in the distance approximation in respect to the measured distance. The detection accuracy was noticed to decrease at long distances and in dark surroundings. Moreover, in order to reduce the effect of the presented errors, the camera should be installed as high as possible without hindering its detection capabilities.

Details

Original languageEnglish
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Publication statusE-pub ahead of print - 9 Aug 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • cameras, intelligent transportation systems, machine vision, neural networks, object detection, vehicle safety

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