24 July 2018 Measuring the absolute distance of a front vehicle from an in-car camera based on monocular vision and instance segmentation
Liqin Huang, Yanan Chen, Zhengjia Fan, Zhifeng Chen
Author Affiliations +
Abstract
The requirements of distance measurement have increased with the development of auto vehicle driving. Traditional methods for distance estimation require the complex calibration from intrinsic and external parameters of the camera. Recent methods based on the neural network structure mainly measure the relative depth of whole images. We adopt monocular vision with instance segmentation and camera focal length to detect the absolute distance of front cars from in-car cameras. First, we extract the location of the cars from the object detection network. Second, the location of cars is sent to the vehicle classification network and instance segmentation network to obtain the type of the cars and their mask value. Here, we use a model trained by the CompCars dataset to classify car types, and we train a new instance segmentation model using the Cityscapes dataset to obtain each car’s mask. Third, in accordance with the camera imaging principle, the absolute distance of cars in the images is calculated based on the relationship between the size information of different car types and their mask values. The proposed method is examined with the KITTI dataset, and the experiment shows that its results can be close to the ground truth. Moreover, the proposed method uses the instance segmentation network to reduce complexity of the depth estimation process and it can still generate satisfactory results even when cars are partly occluded.
© 2018 SPIE and IS&T 1017-9909/2018/$25.00 © 2018 SPIE and IS&T
Liqin Huang, Yanan Chen, Zhengjia Fan, and Zhifeng Chen "Measuring the absolute distance of a front vehicle from an in-car camera based on monocular vision and instance segmentation," Journal of Electronic Imaging 27(4), 043019 (24 July 2018). https://doi.org/10.1117/1.JEI.27.4.043019
Received: 20 March 2018; Accepted: 28 June 2018; Published: 24 July 2018
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CITATIONS
Cited by 17 scholarly publications and 2 patents.
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KEYWORDS
Image segmentation

Cameras

Distance measurement

Imaging systems

Data modeling

Image classification

Network architectures

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