Model-based object tracking in monocular image sequences of road traffic scenes
International Journal of Computer Vision
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
FPGA-based Road Traffic Videodetector
DSD '07 Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools
Robust abandoned object detection using dual foregrounds
EURASIP Journal on Advances in Signal Processing
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Image analysis and rule-based reasoning for a traffic monitoring system
IEEE Transactions on Intelligent Transportation Systems
Computer vision algorithms for intersection monitoring
IEEE Transactions on Intelligent Transportation Systems
Framework for real-time behavior interpretation from traffic video
IEEE Transactions on Intelligent Transportation Systems
Automatic Vehicle Detection Using Local Features—A Statistical Approach
IEEE Transactions on Intelligent Transportation Systems
IEEE Communications Magazine
Journal of Signal Processing Systems
Monocular precrash vehicle detection: features and classifiers
IEEE Transactions on Image Processing
A Robust Object Segmentation System Using a Probability-Based Background Extraction Algorithm
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents a prototype sensor network for monitoring urban traffic. The sensor network node, equipped with a low-resolution camera, observes the street and detects moving objects. Object detection is based on the custom video segmentation algorithm, using dual background subtraction, edge detection and shadow detection, running on dedicated multi-processor SoC hardware. The number and the speed of the detected objects are transmitted using a low-power license-free radio transceiver to another neighboring node. All the nodes create a self-organized network, data are aggregated at the nodes and passed further to the nodes closer to data sinks. Finally, information about the traffic flow is collected from the sinks and visualized on a PC. The prototype sensor network node has been realized in two versions: FPGA and ASIC. The ASIC version consumes approximately 500 mW and it can be powered from a photovoltaic solar panel combined with a single cell Li-Po battery. The comparison of power consumption of both versions has also been made. Apart from collecting traffic data, the proposed sensor network can gather environmental data, such as the temperature, the acoustic noise or the intensity of the sunlight. The set of 26 prototype sensors has been mounted on street lamp-poles on streets and tested in real conditions.