Vehicle detection based on real-time traffic condition and adaptive pixel segmentation

Min SUN; Mian LI; Yu-zhou ZHAO; Wei SUN; Xiao-rui ZHANG

doi:10.37188/CJLCD.2020-0316

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Vehicle detection based on real-time traffic condition and adaptive pixel segmentation

Image Processing | 更新时间：2021-09-30

- Vehicle detection based on real-time traffic condition and adaptive pixel segmentation
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 10, Pages: 1454-1462(2021)
- 作者机构：
  
  1.南京信息工程大学自动化学院, 江苏南京 210044
  2.江苏省大气环境与装备技术协同创新中心, 江苏南京 210044
  3.南京信息工程大学无锡研究院, 江苏无锡 214100
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61304205);National Natural Science Foundation of China(61502240);Natural Science Foundation of Jiangsu Province(BK20191401);Natural Science Foundation of Jiangsu Province(BK20201136);Innovation and Entrepreneurship Training Project of College Students(202010300290);Innovation and Entrepreneurship Training Project of College Students(202010300211);Innovation and Entrepreneurship Training Project of College Students(202010300116E);Excellent graduation design (thesis) support program for undergraduates of NUIST
- DOI：10.37188/CJLCD.2020-0316
  CLC： TP391.41;U463.6
- Received：30 November 2020，
  
  Revised：20 February 2021，
  
  Published：2021-10
- 稿件说明：
移动端阅览
Min SUN, Mian LI, Yu-zhou ZHAO, et al. Vehicle detection based on real-time traffic condition and adaptive pixel segmentation[J]. Chinese journal of liquid crystals and displays, 2021, 36(10): 1454-1462.
DOI：

Min SUN, Mian LI, Yu-zhou ZHAO, et al. Vehicle detection based on real-time traffic condition and adaptive pixel segmentation[J]. Chinese journal of liquid crystals and displays, 2021, 36(10): 1454-1462. DOI： 10.37188/CJLCD.2020-0316.

摘要

车辆检测是车辆识别和跟踪的重要前提。为解决传统车辆检测算法无法兼顾检测的准确性与实时性的问题，本文提出一种基于实时交通状况和自适应像素分割的运动车辆检测算法。该算法采用多帧间隔图像建立初始背景模型，提出基于时-空变化度的背景区域变化评价方法，并基于时-空变化度制订了自适应的学习率更新策略。通过设置一个信任区间，并根据当前交通状况和像素点是否处于信任区间内来判断当前的背景模型是否需要更新，进而实现对运动车辆的准确、快速检测。改进后的像素自适应分割算法在不同场景中检测的性能指标Recall、Precision和F-measure分别达到0.929，0.864，0.888，均高于传统像素自适应分割算法，且算法的处理时间为88.37 ms，比传统像素自适应分割算法的运算速度快近10 ms，基本满足车辆检测所需的速度快、精度高、鲁棒性高等要求。

Abstract

In an intelligent transportation system

the vehicle detection is an important prerequisite for vehicle tracking and identification. However

the traditional vehicle detection algorithms cannot effectively maintain a balance between accuracy and real-time performance. In this paper

a new moving vehicle detection algorithm based on improved adaptive pixel segmentation is proposed. The initial background model is established based on multi frame interval image

and an evaluation method of background region change is proposed based on the spatio-temporal variation degree. Based on this

an adaptive updating strategy of learning rate is formulated. By setting a trust interval

whether the current background model needs to be updated can be adaptively determined according to the current traffic conditions and pixels whether are in the trust interval

thereby the accurate and fast detection of moving vehicles are realized. The performance indicators of Recall

Precision and F-measure of the improved adaptive pixel segmentation algorithm in different scenarios are 0.929

0.864 and 0.888

respectively

which are higher than the traditional adaptive pixel segmentation algorithm

and the processing time of the algorithm is 88.37 ms

nearly 10 ms faster than the traditional adaptive pixel segmentation algorithm. It basically meets the requirements of high speed

high precision and high robustness of vehicle detection.

关键词

Keywords

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