Multi-Traffic Scene Perception Based on Supervised Learning

Traffic accidents are particularly serious on a rainy day, a dark night, an overcast and/or rainy night, a foggy day, and many other times with low visibility conditions. Present vision driver assistance systems are designed to perform under good-natured weather conditions. Classification is a metho...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.4287-4296
Main Authors: Jin, Lisheng, Chen, Mei, Jiang, Yuying, Xia, Haipeng
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classifiers
complex weather conditions
Feature extraction
intelligent vehicle
Low visibility
Machine learning
Machine vision
Meteorology
Night
Object recognition
Optical properties
Roads
Semantics
Supervised learning
Traffic accidents
Underlying visual features
Vehicles
Visual fields
Visualization
Weather
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traffic accidents are particularly serious on a rainy day, a dark night, an overcast and/or rainy night, a foggy day, and many other times with low visibility conditions. Present vision driver assistance systems are designed to perform under good-natured weather conditions. Classification is a methodology to identify the type of optical characteristics for vision enhancement algorithms to make them more efficient. To improve machine vision in bad weather situations, a multi-class weather classification method is presented based on multiple weather features and supervised learning. First, underlying visual features are extracted from multi-traffic scene images, and then the feature was expressed as an eight-dimensions feature matrix. Second, five supervised learning algorithms are used to train classifiers. The analysis shows that extracted features can accurately describe the image semantics, and the classifiers have high recognition accuracy rate and adaptive ability. The proposed method provides the basis for further enhancing the detection of anterior vehicle detection during nighttime illumination changes, as well as enhancing the driver's field of vision on a foggy day.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2790407