Object-Based Vehicle Color Recognition in Uncontrolled Environment

Abstract

The demand for vehicle recognition significantly increases with impact on many businesses in recent decades. This paper focuses on a vehicle color attribute. A novel method for vehicle color recognition is introduced to overcome three challenges of vehicle color recognition. The first challenge is an uncontrolled environment such as shadow, brightness, and reflection. Second, similar color is hard to be taken into account. Third, few research works dedicate to multi-color vehicle recognition. Previous works can provide only color information of the whole vehicle, but not at vehicle part level. In this study, a new approach for recognizing the colors of vehicles at the part level is introduced. It utilizes object detection techniques to identify the colors based on the different objects (e.g. parts of a vehicle in this research). In addition, a novel generic post-processing is proposed to improve robustness in the uncontrolled environment and support not only single-color but also multi-color vehicles. Experimental results show that it can effectively identify the color under the three challenges addressed above with 99 % accuracy for single-color vehicle and outperforms the other seven baseline models, and 76 % accuracy for multi-color vehicle.