A Fast and Robust Multi-color Object Detection Method with Application to Color Chart Detection

Abstract

In this paper, we focus on robust multi-color object detection with cluttered backgrounds and variable illumination for a target application to color chart detection. The task is characterized by a wide range of color variation combined with complex background. Arbitrary placement of the chart in the scene will further complicate the detection task. Conventional methods to this problem normally give an approximate bounding box of the detection result, lacking in an internal geometrical representation. Our method adopts a coarse-to-fine strategy to predict the chart location and recover its accurate topological structure, e.g. the position and boundary of each constituent color area. With this fine detection result, color deviation in the input image can be easily corrected using off-the-shelf softwares such as Photoshop. Experiential results on a public dataset demonstrate that our system can work effectively in real time and give a superior detection rate to the state-of-art. The robustness of this method to large color distortion makes it equally applicable to detection of general multi-color object such as address plate, traffic sign, and so on.