Context-supported Road Information for Background Modeling

Background subtraction methods commonly suffers from incompleteness and instability over many situations. If one treats fast updating when objects run fast, it is not reliable to modeling the background while objects stop in the scene, as well; it is easy to find examples where the contrary is also true. In this paper we propose a novel method - designated Context-supported ROad iNformation (CRON) for unsupervised background modeling, which deals with stationary foreground objects, while presenting a fast background updating. Differently from general-purpose methods, our method was specially conceived for traffic analysis, being stable in several challenging circumstances in urban scenarios. To assess the performance of the method, a thorough analysis was accomplished, comparing the proposed method with many others, demonstrating promising results in our favor.