A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition
作者: Chen WangYulu DaiWei ZhouYifei GengFeng Chen
作者单位: 1Intelligent Transportation Research Center, Southeast University, Nanjing 211189, China
2School of Automation, Southeast University, Nanjing 211189, China
刊名: Journal of Advanced Transportation, 2020, Vol.2020
来源数据库: Hindawi Journal
DOI: 10.1155/2020/9194028
原始语种摘要: In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed...
全文获取路径: Hindawi 

  • crash 事故
  • proposed 建议的
  • vehicle 
  • detection 探测
  • visibility 可见度
  • empirical 经验的
  • decision 决定
  • framework 构架
  • quickly 快速地
  • conditions 条件式