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Paper Title : TRAFFIC PERCEPTION BASED ON WEATHER CONDITION

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.7221288

Authors: Dr.B.Subba Reddy, K.Harshitha, M.Sai Akshaya, M. Manasa

         


Abstract
— Traffic accidents are particularly serious on the rainy days,the dark nights, the rainy nights, the foggy day, and many other times with low visibility conditions. Existing vision driver assistance systems are designed to perform under good-natured weather conditions. Classification is a type of methodology to identify the optical characteristics for vision enhancement algorithms to make them more efficient. To improve the machine vision at bad weather situations, a multi-class weather classification method has been presented based on multiple weather features and supervised learning. Priorly underlying visual features are extracted from multi-traffic scene images.Then secondly, five supervised learning algorithms are used to train classifiers. The analysis shows about how extracted features can accurately describe the image semantics, and classifiers have high recognition accuracy rate and adaptive ability. The proposed method will be providing the basis for further enhancing the detection of anterior vehicle detection during the night time illumination changes, and enhancing the driver’s field of vision on a foggy day.

Reference
[1] M. Milford, E. Vig, W. Scheirer, and D. Cox, ‘‘Vision-based simultaneous localization and mapping in changing outdoor environments,’’ J. Field Robot., vol. [2] C.-Y. Fang, S.-W. Chen, and C.-S. Fuh, ‘‘Automatic change detection of driving environments in a vision-based driver assistance system,’’ IEEE Trans. [3] Y. J. Liu, C. C. Chiu, and J. H. Yang, ‘‘A robust vision-based skyline detection algorithm under different weather conditions,’’ [4] K. Wang, Z. Huang, and Z. Zhong, ‘‘Simultaneous multi-vehicle detection and tracking framework with pavement constraints based on machine learning and particle filter algorithm,’’.
Keywords
— Machine Learning,Unsupervised Learning, Python, Digital Image Processing, Support Vector Machine Algorithm.