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Autonomous video surveillance application using artificial vision to track people in restricted areas

Abstract:

The present project implements an application for the search, recognition and monitoring of people based on artificial vision algorithms. The OpenCV libraries are used to process the images, which were obtained from a conventional IP video surveillance camera. This type of cameras can be used in different environmental conditions (high, medium and low lighting) and up to an effective distance of 70 m. In the detection and search phase, cascade classifiers are used with local binary patterns LBP (Local Binary Patterns). Subsequently, in the follow-up phase, a tracking algorithm is implemented, addressed only to the person detected through kernelized correlation filters KCF (Kernelized Correlation Filters), so that the objective is not lost. A graphical interface was developed in the Qt Software which allows an easy use of the application. The average effectiveness of the algorithm is 90% in different environments and places by mitigating the different luminosity changes.