Browsing by Subject "driving video"

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    Big-video mining of road appearances in full spectrums of weather and illuminations
    (IEEE, 2017-10) Cheng, Guo; Wang, Zheyuan; Zheng, Jiang Yu; Computer and Information Science, School of Science
    Autonomous and safety driving require the control of vehicles within roads. Compared to lane mark tracking, road edge detection is more difficult because of the large variation in road and off-road materials and the influence from weather and illuminations. This work investigates visual appearances of roads under a spectrum of weather conditions. We use big-data mining on large scale naturalistic driving videos taken over a year through four seasons. Large video volumes are condensed to compact road profile images for analysis. Clusters are extracted from all samples with unsupervised learning. Typical views of a spectrum of weather/illuminations are generated from the clusters. Further, by changing the number of clusters we find a stable number for clustering. The learned data are used to classify driving videos into typical illumination types briefly. The surveyed data can also be used in the development of road edge detection algorithm and system as well as their testing.
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    Visualizing Road Appearance Properties in Driving Video
    (IEEE, 2016-07) Wang, Zheyuan; Zheng, Jiang Yu; Kilicarslan, Mehmet; Department of Computer & Information Science, School of Science
    With the increasing videos taken from driving recorders on thousands of cars, it is a challenging task to retrieve these videos and search for important information. The goal of this work is to mine certain critical road properties in a large scale driving video data set for traffic accident analysis, sensing algorithm development, and testing benchmark. Our aim is to condense video data to compact road profiles, which contain visual features of the road environment. By visualizing road edge and lane marks in the feature space with the reduced dimension, we will further explore the road edge models influenced by road and off-road materials, weather, lighting condition, etc.