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We present a unified system for vehicle tracking and classification which has been developed with a data-driven approach on real-world data. The main purpose of the system is the tracking of the vehicles to understand lane changes, gates transits and other behaviors useful for traffic analysis. The discrimination of the vehicles into two classes (cars vs. trucks) is also required for electronic truck-tolling. Both tracking and classification are performed online by a system made up of two components (tracker and classifier) plus a controller which automatically adapts the configuration of the system to the observed conditions. Experiments show that the proposed system outperforms the state-of-the-art algorithms on the considered data. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to dynamically manage the traffic lights cycles and phases in an isolated intersection. The target of the work is a system that, comparing with previous solutions, offers improved performance, is flexible and can be implemented on off-the-shelf components. The challenge here is to find an effective design that achieves the target while avoiding complex and computationally expensive solutions, which would not be appropriate for the problem at hand and would impair the practical applicability of the approach in real scenarios. The proposed solution is a traffic lights dynamic control system that combines an IEEE 802.15.4 Wireless Sensor Network (WSN) for real-time traffic monitoring with multiple fuzzy logic controllers, one for each phase, that work in parallel. Each fuzzy controller addresses vehicles turning movements and dynamically manages both the phase and the green time of traffic lights. The proposed system combines the advantages of the WSN, such as easy deployment and maintenance, flexibility, low cost, noninvasiveness, and scalability, with the benefits of using four parallel fuzzy controllers, i.e., better performance, fault-tolerance, and support for phase-specific management. Simulation results show that the proposed system outperforms other solutions in the literature, significantly reducing the vehicles waiting times. A proof-of-concept implementation on an off-the-shelf device proves that the proposed controller does not require powerful hardware and can be easily implemented on a low-cost device, thus paving the way for extensive usage in practice. [ABSTRACT FROM AUTHOR]

Abstract: A relevant aspect when evaluating the city smartness is related to the innovative approach to urban traffic management. We present a system called city kernel, designed to handle several subsystems, each addressing a specific sensor network and we describe an infrastructure for wide traffic control via a vision sensor network. This infrastructure consists of a network of smart cameras operating over an outdoor public lighting thanks to power line communication technology and equipped with a vehicle counting and classification algorithm. We discuss the deployment of this network on the city kernel and some related services exposed to urban actors. [Copyright &y& Elsevier]

Abstract: An innovative system for detecting and extracting vehicles in traffic surveillance scenes is presented. This system involves locating moving objects present in complex road scenes by implementing an advanced background subtraction methodology. The innovation concerns a histogram-based filtering procedure, which collects scatter background information carried in a series of frames, at pixel level, generating reliable instances of the actual background. The proposed algorithm reconstructs a background instance on demand under any traffic conditions. The background reconstruction algorithm demonstrated a rather robust performance in various operating conditions including unstable lighting, different view-angles and congestion. [ABSTRACT FROM AUTHOR]

Abstract: This article presents a visual application which allows a study and analysis of traffic behavior on major roads (more specifically freeways and highways), using as the main surveillance artefact a video camera mounted on a relatively high place (such as a bridge) with a significant image analysis field. The system described presents something new which is the combination of both traditional traffic monitoring systems, that is, monitoring to get information on different traffic parameters and monitoring to detect accidents automatically. Therefore, we present a system in charge of compiling information on different traffic parameters. It also has a surveillance module for that traffic, which can detect a wide range of the most significant incidents on a freeway or highway. [Copyright &y& Elsevier]