A method based on visual saliency for vehicle-mounted monocular camera ego-motion estimation and vehicle scale estimation

Mingxin AI; Tie LIU; Jing WANG; Jiali DING; Zejian YUAN; Yuanyuan SHANG

doi:10.11959/j.issn.2096-6652.202129

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A method based on visual saliency for vehicle-mounted monocular camera ego-motion estimation and vehicle scale estimation

Special Issue: Intelligent Object Detection and Recognition | 更新时间：2024-06-05

- A method based on visual saliency for vehicle-mounted monocular camera ego-motion estimation and vehicle scale estimation
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 3, Pages: 280-293(2021)
- 作者机构：
  
  1. 首都师范大学信息工程学院，北京 100048
  2. 西安交通大学人工智能学院，陕西西安 710049
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202129
  CLC： TP181
- Published Online：2021-09，
  
  Published：15 September 2021
- 稿件说明：
移动端阅览
Mingxin AI, Tie LIU, Jing WANG, et al. A method based on visual saliency for vehicle-mounted monocular camera ego-motion estimation and vehicle scale estimation[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 280-293.
DOI：

Mingxin AI, Tie LIU, Jing WANG, et al. A method based on visual saliency for vehicle-mounted monocular camera ego-motion estimation and vehicle scale estimation[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 280-293. DOI： 10.11959/j.issn.2096-6652.202129.

摘要

提出一种基于视觉显著性的车载单目相机自运动估计及前车尺度估计方法。首先，针对车载相机自运动估计，通过视觉显著性计算方法检测并去除含有噪声的单目图像序列中的运动目标，同时考虑图像的纹理区域和平滑区域，利用加权显著图保留有用特征点，进而对车载相机进行鲁棒的自运动估计。其次，将前车距离转化为前车尺度估计问题，通过描述子匹配与李代数中正则化的强度匹配相结合的方法最小化损失函数，通过设计视觉注意力机制选择有纹理无遮挡的图像块，并对选定的图像块中的像素赋权以减轻被噪声破坏像素的影响，从而实现鲁棒、准确的尺度估计。最后，利用多个具有挑战性的数据集对所提方法进行分析验证。结果表明，单目相机自运动估计方法达到了基于立体相机方法的水平，前车尺度估计方法在充分发挥强鲁棒性优势的同时保证了预测精度。

Abstract

A method based on visual saliency for ego-motion estimation and scale estimation of the vehicle in front was proposed.Firstly

for the ego-motion estimation of vehicle-mounted camera

the visual saliency calculation method was used to detect and remove moving objects in the monocular image sequence containing noise.While considering the image texture and smooth region

the weighted saliency map was used to retain useful feature points

to improve the robustness of ego-motion estimation.Secondly

the distance of the vehicle in front was converted into a vehicle scale estimation

by integrating descriptor match and the strength of regularization match of the lie algebra to minimize loss function.The visual attention mechanism was used to get texture image block without shade

and the pixel in the image block weight to mitigate the effects of destroyed by noise pixel

so as to realize the robust and accurate scale estimation.Finally

several challenging datasets were used to analyze and verify the proposed method.The results show that the monocular camera ego-motion estimation method reaches the level of the stereo camera-based method

and the vehicle scale estimation method ensures the prediction accuracy while giving full play to the advantages of strong robustness.

关键词

Keywords

references

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