@inproceedings{edd7fe7204e543f4a5552e26bb54d568,
title = "Pedestrian detection in haze environments using dark channel prior and histogram of oriented gradient",
abstract = "Pedestrian detection is a critical requirement for industry computer vision systems and has great research value. Many techniques have been proposed, and one of the most effective methods is based on the Histogram of Oriented Gradient (HOG) descriptor and the Support Vector Machine (SVM) classifier. While this method implicitly assumes that the input images are taken in haze-free environments. In this paper, we propose a new method that is capable of handling pedestrian detection in haze environments. Firstly, a haze version of 'INRIA' data set is synthesized based on the natural light transport model. Secondly, robust HOG descriptors are calculated by a fusion model which relies on the dark channel prior haze removal method. At last, a linear SVM classifier is trained for pedestrian detection. The proposed method outperforms the traditional HOG pedestrian detection method on the haze version of 'INRIA' data set.",
keywords = "Dark channel, Haze removal, Hog, Pedestrian detection, Svm",
author = "Baoyang Ding and Zhenghua Liu and Yang Sun",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 8th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2018 ; Conference date: 19-07-2018 Through 21-07-2018",
year = "2018",
month = jul,
doi = "10.1109/IMCCC.2018.00211",
language = "英语",
series = "Proceedings - 8th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1003--1008",
editor = "Jun-Bao Li",
booktitle = "Proceedings - 8th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2018",
address = "美国",
}