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German, R., Laura, S., Materzynska, J., Vazquez, D. and Lopez, A.M. (2016) The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 3234-3243.

has been cited by the following article:

• TITLE: Efficient Object Localization Scheme Based on Vanishing Line in Road Image for Autonomous Vehicles

  AUTHORS: Bongkyo Moon, Jiwon Choi, Juehyun Lee, Minyoung Lee

  KEYWORDS: Object Localization, Image Preprocessing, Sliding Window, Vanishing Line, Autonomous Vehicle

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.9, September 27, 2021

  ABSTRACT: This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the time complexity of O(n) while the existing sliding window method requires the time complexity O(n2) for detecting all objects in the entire image. In addition, the range of detection area can be also remarkably reduced when compared with the sliding window method. As a result, the total range and times for searching in the proposed method can be significantly reduced by considering together the distance and position of the object. The experiment on the proposed method is performed with the virtual road data set known as SYNTHIA, and the competitive results are obtained.