Abstract
Public safety is, to a greater or lesser extent, a significant concern in most modern cities. In many of these cities, video surveillance is employed to prevent and deter crime, often building systems with hundreds of cameras and sensors. Such systems proved to be effective in crime fighting and prevention, but they have high bandwidth requirements in order to bring a real-time monitoring. In countryside areas, where only low speed connections are available, the installation of such systems is not suitable and a new approach is required.
In this context, this project proposes a platform based on open source libraries for video analysis techniques such as motion detection, object tracking, object classification on a low-bandwidth network.
The proposed architecture is open and scalable as a result of performing image processing on the camera. These platform runs on robust distributed smart cameras that are ready for being installed in hard places. The system is prepared to deal with energy power failures; in order to increase reliability.
This work describes the platform design, the smartCAM layout and components, the algorithms currently used for object tracking and classification, and exposes results regarding the efficiency of the solution.
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Acknowledgements
This project is based on the connectivity infrastructure provided by the ‘Universidad Nacional del Centro de la Prov. de Bs. As (UNCPBA)’. The current project has received subsidies from the ‘Comisión de Investigaciones Científicas’ of Argentina.
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D‘Amato, J.P., Perez, A., Dominguez, L., Rubiales, A., Barbuzza, R., Stramana, F. (2018). Video Analytics on a Mixed Network of Robust Cameras with Processing Capabilities. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-77703-0_75
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DOI: https://doi.org/10.1007/978-3-319-77703-0_75
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