Smart Surveillance

Computer Vision problems applied to visual surveillance have been studied for several years with the aim of finding accurate and efficient solutions, which are required to allow the execution of surveillance systems in real environments. The main goal of such systems is to analyze the scene focusing on the detection and recognition of suspicious activities performed by humans in the scene, so that the security personnel can pay closer attention to these preselected activities. To accomplish that, several problems have to be solved first, for instance background subtraction, person detection, tracking and re-identification, face recognition, and action  recognition. Even though each of these problems has been researched in the past decades, they are hardly considered in a sequence, each one is usually solved individually. However, in real surveillance scenarios, the aforementioned problems have to be solved in sequence considering only videos as the input.


Related Publications

Raphael Felipe Carvalho de Prates

Matching People Across Surveillance Cameras PhD Thesis

Universidade Federal de Minas Gerais, 2019.

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Antonio Carlos Nazare Junior; William Robson Schwartz

A scalable and flexible framework for smart video surveillance Journal Article

In: Computer Vision and Image Understanding, 144 (C), pp. 258–275, 2016.

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Antonio Carlos Nazare Junior

A Scalable and Versatile Framework for Smart Video Surveillance Masters Thesis

Federal University of Minas Gerais, 2014.

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Antonio Carlos Nazare Junior; Cassio Elias Santos dos Junior; Renato Ferreira; William Robson Schwartz

Smart Surveillance Framework: A Versatile Tool for Video Analysis Inproceedings

In: IEEE Winter Conference on Applications of Computer Vision, pp. 753–760, 2014.

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Antonio Carlos Nazare Junior; Renato Ferreira; William Robson Schwartz

Scalable Feature Extraction for Visual Surveillance Inproceedings

In: Iberoamerican Congress on Pattern Recognition (CIARP), pp. 375-382, Springer International Publishing, 2014.

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