Vision based automatic traffic condition interpretation

Traffic flow, analysis and control is gaining high relevance, as the number of circulating vehicles continuously increases. This article proposes a computer vision based platform, which automatically detects vehicles in order to infer the traffic conditions. The developed real time detection algorithm is based on a dual background subtraction technique, incorporating the one known has Codebook and an edges one. These two layers interact mutually allowing the compensation of individuality weaknesses. The traffic flow parameters are extracted comparing the detected vehicles with a known model of the road lanes, which can be automatically generated based on the vehicles trajectory analysis over time. The achieved results demonstrate that the developed algorithm is able to correctly understand the traffic flow state, even in the presence of adverse situations that are typical of an outdoor application.