Nowadays, development is synonymous with construction of infrastructure. Such road infrastructure needs constant attention in terms of traffic monitoring as even a single disaster on a major artery will disrupt the way of life. Humans cannot be expected to monitor these massive infrastructures over 24/7 and computer vision is increasingly being used to develop automated strategies to notify the human observers of any impending slowdowns and traffic bottlenecks. However, due to extreme costs associated with the current state of the art computer vision based networked monitoring systems, innovative computer vision based systems can be developed which are standalone and efficient in analyzing the traffic flow and tracking vehicles for speed detection. In this article, a traffic monitoring system is suggested that counts vehicles and tracks their speeds in realtime for multi-track freeways in Australia. Proposed algorithm uses Gaussian mixture model for detection of foreground and is capable of tracking the vehicle trajectory and extracts the useful traffic information for vehicle counting. This stationary surveillance system uses a fixed position overhead camera to monitor traffic.
Abstract. Vision-based vehicle detection and segmentation in intelligent transportation systems, particularly under outdoor illuminations, camera vibration, cast shadows and vehicle variations is still an area of active research for analysis and processing of traffic data. This paper proposes an effective scheme that improves Gaussian mixture model (GMM) for non-stationary temporal distributions through dynamically updating the learning rate at each pixel. In this proposed technique, sleeping foreground pixels and slow moving vehicles cannot become the part of background model that also does not lead to extra computational cost as compare to other methods that are proposed in the literature. Sudden illumination change is also captured in this technique. Vision based system cannot be efficient without fixing of camera vibration, so movement of camera is adjusted based on clues from background model. At the end, shadows are removed from detected vehicles through applying a new recursive method in dark regions. Experimental results demonstrate the robustness and high level performance of the proposed adaptive foreground extraction algorithm under illumination variations compared to state-of-the-art methods.
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