Weather Recognition Based on Images Captured.pdf

Weather Recognition Based on Images Captured.pdf

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Weather Recognition Based on Images Captured

Weather Recognition Based on Images Captured by Vision System in Vehicle Xunshi Yan1, Yupin Luo1, and Xiaoming Zheng2 1 Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China 2 INF Technologies, Ltd. , Beijing 100086, China yanxs06@mails.thu.edu.cn,luo@tsinghua.edu.cn,zheng@tsinghua.edu.cn Abstract. Weather recognition is widely required in many areas, and it is also a challenging and brand-new subject. This paper proposes an approach to recognize weather based on images captured by in-vehicle vision system. We bring three groups of features, including histogram of gradient amplitude, HSV color histogram, road information, and employ an algorithm based on Real AdaBoost, making use of the category struc- ture to achieve the task of classification. Experiments confirm superior performances on our dataset collected from images captured by vision system. Keywords: Weather recognition, Vision system, Real AdaBoost, HSV color space, Category structure. 1 Introduction Computer vision system has achieved great success in many areas, such as surveillance, navigation, driver assistance system. However, the cameras exposed outside are easily influenced by bad weather. For example, pedestrian detection system in vehicle could not work at all when raindrop falls on the camera, even results serious false-detection. Many vision systems also need to reset parameters such as lighting, rain wiper, under different weather conditions. Hence, research of weather recognition in vision system is in urgent demand. Weather recognition is a brand-new subject and only a few of previous work has addressed this issue. Garg and Naya[1] in Columbia University focus on detecting and removing rain streaks from videos. The idea comes from moving object detection. It makes a difference between the two adjacent frames, and can give some perfect results under certain scenarios, but it is hard to satisfy the dyna

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