Vehicle Color Recognition With Spatial Pyramid Deep Learning

• Published in: IEEE transactions on intelligent transportation systems 2015-10, Vol.16 (5), p.2925-2934
• Main Authors: Hu, Chuanping, Bai, Xiang, Qi, Li, Chen, Pan, Xue, Gengjian, Mei, Lin
• Format: Article
• Language: English
• Subjects: Color recognition; Computer architecture; convolutional neural network (CNN); deep learning; Feature extraction; Histograms; Image color analysis; intelligent transportation; Neural networks; spatial pyramid (SP); Support vector machines; Training; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2015.2430892

Color, as a notable and stable attribute of vehicles, can serve as a useful and reliable cue in a variety of applications in intelligent transportation systems. Therefore, vehicle color recognition in natural scenes has become an important research topic in this area. In this paper, we propose a deep-learning-based algorithm for automatic vehicle color recognition. Different from conventional methods, which usually adopt manually designed features, the proposed algorithm is able to adaptively learn representation that is more effective for the task of vehicle color recognition, which leads to higher recognition accuracy and avoids preprocessing. Moreover, we combine the widely used spatial pyramid strategy with the original convolutional neural network architecture, which further boosts the recognition accuracy. To the best of our knowledge, this is the first work that employs deep learning in the context of vehicle color recognition. The experiments demonstrate that the proposed approach achieves superior performance over conventional methods.