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Volume: 33 | Article ID: art00013
Methods and Comparisons Between Computer Vision and Radar Based Vehicle Location
  DOI :  10.2352/ISSN.2470-1173.2021.6.IRIACV-334  Published OnlineJanuary 2021

Measuring vehicle locations relative to a driver's vehicle is a critical component in the analysis of driving data from both postanalysis (such as in naturalistic driving studies) or in autonomous vehicle navigation. In this work we describe a method to estimate vehicle positions from a forward-looking video camera using intrinsic camera calibration, estimates of extrinsic parameters, and a convolutional neural network trained to detect and locate vehicles in video data. We compare the measurements we achieve with this method with ground truth and with radar data available from a naturalistic driving study. We identify regions where video is preferred, where radar is preferred, and explore trade-offs between the two methods in regions where the preference is more ambiguous. We describe applications of these measurements for transportation analysis.

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Deniz Aykac, Regina Ferrell, Nisha Srinivas, Thomas Karnowski, "Methods and Comparisons Between Computer Vision and Radar Based Vehicle Locationin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2021,  pp 334-1 - 334-7,

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