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Tehrani, H., Akihiro, T., Mita, S. and Mcallester, D.A. (2012) On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter with Improved Likelihood Estimation. IEEE Transactions on Intelligent Transportation, 2, 748-758. https://doi.org/10.1109/TITS.2012.2187894

has been cited by the following article:

• TITLE: A Vehicle Detection Method for Aerial Image Based on YOLO

  AUTHORS: Junyan Lu, Chi Ma, Li Li, Xiaoyan Xing, Yong Zhang, Zhigang Wang, Jiuwei Xu

  KEYWORDS: Vehicle Detection, Aerial Image, YOLO, VEDAI, COWC, DOTA

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.11, November 19, 2018

  ABSTRACT: With the application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become a key engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial image based on YOLO deep learning algorithm is presented. The method integrates an aerial image dataset suitable for YOLO training by pro-cessing three public aerial image datasets. Experiments show that the training model has a good performance on unknown aerial images, especially for small objects, rotating objects, as well as compact and dense objects, while meeting the real-time requirements.