Monocular Video-Based Trailer Coupler Detection Using Multiplexer Convolutional Neural Network

Yousef Atoum, Joseph Roth, Michael Bliss, Wende Zhang, Xiaoming Liu; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 5477-5485

Abstract


This paper presents an automated monocular-camera-based computer vision system for autonomous self-backing-up a vehicle towards a trailer, by continuously estimating the 3D trailer coupler position and feeding it to the vehicle control system, until the alignment of the tow hitch with the trailers coupler. This system is made possible through our proposed distance-driven Multiplexer-CNN method, which selects the most suitable CNN using the estimated coupler-to-vehicle distance. The input of the multiplexer is a group made of a CNN detector, trackers, and 3D localizer. In the CNN detector, we propose a novel algorithm to provide a presence confidence score with each detection. The score reflects the existence of the target object in a region, as well as how accurate is the 2D target detection. We demonstrate the accuracy and efficiency of the system on a large trailer database. Our system achieves an estimation error of 1.4 cm when the ball reaches the coupler, while running at 18.9 FPS on a regular PC.

Related Material


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[bibtex]
@InProceedings{Atoum_2017_ICCV,
author = {Atoum, Yousef and Roth, Joseph and Bliss, Michael and Zhang, Wende and Liu, Xiaoming},
title = {Monocular Video-Based Trailer Coupler Detection Using Multiplexer Convolutional Neural Network},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}