Colour fast-match for precise vehicle retrieval

Abstract

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.

Keywords

• image classification
• image retrieval
• image matching
• image colour analysis
• feature extraction
• computer vision
• intelligent transportation systems
• precise vehicle retrieval
• fast affine matching colour image retrieval method
• annual inspection label area
• reidcar dataset
• vehicleid dataset
• data scale
• image quality
• visual attributes
• intelligent traffic system
• vision community