Computer Vision Techniques for Background Modelling in Urban Traffic Monitoring

Traffic data collection is an essential issue for road-traffic control departments, which need real-time information for traffic-parameter estimation: road-traffic intensity, lane occupancy, congestion level, estimation of journey times, etc., as well as for early incident detection. This information can be used to improve road safety as well as to make an optimal use of the existing infrastructure or to estimate new infrastructure needs. In an intelligent transportation system, traffic data may come from different kinds of sensors. The use of video cameras (many of which are already installed to survey road networks), coupled with computer vision techniques, offers an attractive alternative to other traffic sensors (Michalopoulos, 1991). For instance, they can provide powerful processing capabilities for vehicle tracking and classification, providing a non-invasive and easier to install alternative to traditional loop detectors (Fathy & Siyal, 1998; Ha et al., 2004). Successful video-based systems for urban traffic monitoring must be adaptive to different traffic or environmental conditions (Zhu & Xu, 2000; Zhou et al., 2007). Key aspects to be considered are motion-based foreground/background segmentation (Piccardi, 2004; Beymer et al., 2007; Kanhere & Birchfield, 2008), shadow removal algorithms (Prati et al., 2003; Cucchiara et al., 2003), and mechanisms for providing relative robustness against progressive or sudden illumination changes. These video-based systems have to deal with specific difficulties in urban traffic environments, where dense traffic flow, stop-and-go motion profiles, vehicle queues at traffic lights or intersections, etc., would be expected to occur. This chapter is focused on background subtraction, which is a very common technique for detecting moving objects from image sequences using a static camera. The idea consists of extracting moving objects as the foreground elements obtained from the “difference” image between each frame and the so-called background model of the scene (Spagnolo et al., 2006). This model is used as a reference image to be compared with each recorded image. Consequently, the background model must be an accurate representation of the scene after removing all the non-stationary elements. It must be permanently updated to take into account the eventual changes in the lighting conditions or in the own background contents. Surveys and comparisons of different algorithms for background subtraction can be found in the literature (Piccardi, 2004; Chalidabhongse, 2003; Cheung & Kamath, 2004). Regarding to the category of parametric background subtraction algorithms, in the simplest case, it is assumed that each background pixel can be modelled by a single unimodal