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Abstract

Dashboard camera installations are becoming increasingly common due to various Advanced Driver Assistance Systems (ADAS) based services provided by them. Though deployed primarily for crash recordings, calibrating these cameras can allow them to measure real-world distances, which can enable a broad spectrum of ADAS applications such as lane-detection, safe driving distance estimation, collision prediction, and collision prevention Today, dashboard camera calibration is a tedious manual process that requires a trained professional who needs to use a known pattern (e.g., chessboard-like) at a calibrated distance. In this paper, we propose SmartDash-Cam, a system for automatic and live calibration of dashboard cameras which always ensures highly accurate calibration values. Smart-DashCam leverages collecting images of a large number of vehicles appearing in front of the camera and using their coarse geometric shapes to derive the calibration parameters. In sharp contrast to the manual process we are proposing the use of a large amount of data and machine learning techniques to arrive at calibration accuracies that are comparable to the manual process. SmartDashCam implemented using commodity dashboard cameras estimates realworld distances with mean errors of 5.7 % which closely rivals the 4.1% mean error obtained from traditional manual calibration using known patterns.


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The different versions of the original document can be found in:

https://dl.acm.org/doi/pdf/10.1145/3302506.3310397,
https://academic.microsoft.com/#/detail/2930940030
http://dx.doi.org/10.1145/3302506.3310397 under the license http://www.acm.org/publications/policies/copyright_policy#Background
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Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1145/3302506.3310397
Licence: Other

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