Smart Surveillance

Smart Surveillance

Computer Vision problems applied to visual surveillance have been studied for several years aiming at finding accurate and efficient solutions, 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 have been researched in the past decades, they are hardly considered in a sequence, each one is usually solved individually. However, in a real surveillance scenarios, the aforementioned problems have to be solved in sequence considering only videos as the input.

Software

  • Smart Surveillance Framework (SSF) available for download here.

 

Master’s Theses

Antonio Carlos Nazaré Jr. (2014): A Scalable and Versatile Framework for Smart Video Surveillance. Federal University of Minas Gerais, 2014. (Type: Mastersthesis | Abstract | BibTeX | Links: )

 

Publications

1.Jr., Antonio C. Nazare; Schwartz, William Robson (2016): A scalable and flexible framework for smart video surveillance. In: Computer Vision and Image Understanding, 144 (C), pp. 258–275, 2016. (Type: Article | Links | BibTeX)
2.Jr., Antonio Carlos Nazaré (2014): A Scalable and Versatile Framework for Smart Video Surveillance. Federal University of Minas Gerais, 2014. (Type: Mastersthesis | Abstract | Links | BibTeX)
3.Jr, A C Nazare; Jr, C E Santos; Ferreira, Renato; Schwartz, W R (2014): Smart Surveillance Framework: A Versatile Tool for Video Analysis. In: IEEE Winter Conference on Applications of Computer Vision, pp. 753–760, 2014. (Type: Inproceeding | Links | BibTeX)
4.Jr., Nazare, Antonio C.,; Renato, Ferreira,; Robson, Schwartz, William (2014): Scalable Feature Extraction for Visual Surveillance. In: Iberoamerican Congress on Pattern Recognition (CIARP), pp. 375-382, Springer International Publishing, 2014. (Type: Inproceeding | Links | BibTeX)