Open AccessColour fast‐match for precise vehicle retrieval
Author(s) -
Wei Jian,
Wang Yue,
Liu Feng,
Lin Qiuli,
Wang Ning
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0882
Subject(s) - computer science , artificial intelligence , affine transformation , computer vision , hue , feature (linguistics) , image retrieval , task (project management) , visual word , matching (statistics) , pattern recognition (psychology) , image (mathematics) , mathematics , engineering , linguistics , philosophy , statistics , systems engineering , pure mathematics
The explosive growth of vehicles has increased the importance of intelligent traffic system. However, compared with face recognition, vehicle retrieval has not attracted the attention of researchers in vision community. Precise vehicle retrieval has always been a challenging task because it requires the retrieval of all vehicles with the same visual attributes from a large number of vehicles with subtle visual differences. To handle this, the authors propose to implement precise vehicle retrieval using an improved fast affine matching colour image retrieval method based on the features of annual inspection label area. Moreover, regional colour constant and hue and saturation feature are introduced to the proposed method so as to settle the illumination change problem in the real surveillance scene. To fully evaluate the proposed algorithm, they perform experiments on the ReIDcar and VehicleID datasets, which differ in data scale and image quality. The experimental results show that the presented algorithm outperforms traditional methods in vehicle retrieval. It is verified that the extracted features and the feature matching method can distinguish subtle differences between vehicles.