This paper describes the FPGA-based hardware implementation of an algorithm for an automatic traffic surveillance sensor network. The aim of the algorithm is to extract moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed, using low power hardware of a sensor network node. A single, stationary, monochrome camera is used, mounted at a location high above the road. Occlusions are not detected, however simple shadow and highlight elimination is performed. The algorithm is designed for frame-rate efficiency and is specially suited for pipelined hardware implementation. The authors, apart from the careful selection of particular steps of the algorithm and the modifications towards parallel implementation, also proposed novel improvements such as backgrounds' binary mask combination or non-linear functions in highlight detection, resulting in increasing the robustness and efficiency of hardware realization. The algorithm has been implemented in FPGA and tested on real-time video streams from an outdoor camera.
Abstract. This paper describes the idea and the implementation of the image detection algorithm, that can be used in integrated sensor networks for environment and traffic monitoring in urban areas. The algorithm is dedicated to the extraction of moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed. The authors, apart from the careful selection of particular steps of the algorithm towards hardware implementation, also proposed novel improvements, resulting in increasing the robustness and the efficiency. A single, stationary, monochrome camera is used, simple shadow and highlight elimination is performed. The occlusions are not taken into account, due to placing the camera at a location high above the road. The algorithm is designed and implemented in pipelined hardware, therefore high frame-rate efficiency has been achieved. The algorithm has been implemented and tested in FPGA and ASIC.
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.
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