Antonio Carlos Nazare Junior A Scalable and Versatile Framework for Smart Video Surveillance Masters Thesis Federal University of Minas Gerais, 2014. Resumo | Links | BibTeX @mastersthesis{Nazare:2014:MSc,
title = {A Scalable and Versatile Framework for Smart Video Surveillance},
author = {Antonio Carlos Nazare Junior},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2014_Antonio-1.pdf},
year = {2014},
date = {2014-09-05},
school = {Federal University of Minas Gerais},
abstract = {The availability of surveillance cameras placed in public locations has increased vastly in the last years, providing a safe environment for people at the cost of huge amount of visual data collected. Such data are mostly processed manually, a task which is labor intensive and prone to errors. Therefore, automatic approaches must be employed to enable the processing of the data, so that human operators only need to reason about selected portions.
Focused on solving problems in the domain of visual surveillance, computer vision problems applied to this domain have been developed 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 staff can pay closer attention to these preselected activities. However these systems are rarely tackled in a scalable manner.
Before developing a full surveillance system, 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 scenario, the aforementioned problems have to be solved in sequence considering only videos as the input.
Aiming at the direction of evaluating approaches in more realistic scenarios, this work proposes a framework called Smart Surveillance Framework (SSF), to allow researchers to implement their solutions to the above problems as a sequence of processing modules that communicates through a shared memory.
The SSF is a C++ library built to provide important features for a surveillance system, such as a automatic scene understanding, scalability, real-time operation, multi-sensor environment, usage of low cost standard components, runtime re-configuration, and communication control.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
The availability of surveillance cameras placed in public locations has increased vastly in the last years, providing a safe environment for people at the cost of huge amount of visual data collected. Such data are mostly processed manually, a task which is labor intensive and prone to errors. Therefore, automatic approaches must be employed to enable the processing of the data, so that human operators only need to reason about selected portions.
Focused on solving problems in the domain of visual surveillance, computer vision problems applied to this domain have been developed 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 staff can pay closer attention to these preselected activities. However these systems are rarely tackled in a scalable manner.
Before developing a full surveillance system, 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 scenario, the aforementioned problems have to be solved in sequence considering only videos as the input.
Aiming at the direction of evaluating approaches in more realistic scenarios, this work proposes a framework called Smart Surveillance Framework (SSF), to allow researchers to implement their solutions to the above problems as a sequence of processing modules that communicates through a shared memory.
The SSF is a C++ library built to provide important features for a surveillance system, such as a automatic scene understanding, scalability, real-time operation, multi-sensor environment, usage of low cost standard components, runtime re-configuration, and communication control. |
Victor Hugo Cunha de Melo Fast and Robust Optimization Approaches for Pedestrian Detection Masters Thesis Federal University of Minas Gerais, 2014. Resumo | Links | BibTeX @mastersthesis{Melo:2014:MSc,
title = {Fast and Robust Optimization Approaches for Pedestrian Detection},
author = {Victor Hugo Cunha de Melo},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/dissertation_2014_Victor.pdf},
year = {2014},
date = {2014-02-28},
school = {Federal University of Minas Gerais},
abstract = {The large number of surveillance cameras available nowadays in strategic points of major cities provides a safe environment. However, the huge amount of data provided by the cameras prevents its manual processing, requiring the application of automated methods. Among such methods, pedestrian detection plays an important role in reducing the amount of data by locating only the regions of interest for further processing regarding activities being performed by agents in the scene. However, the currently available methods are unable to process such large amount of data in real time. Therefore, there is a need for the development of optimization techniques. Towards accomplishing the goal of reducing costs for pedestrian detection, we propose in this work two optimization approaches. The first approach consists of a cascade of rejection based on Partial Least Squares (PLS) combined with the propagation of latent variables through the stages. Our results show that the method reduces the computational cost by increasing the number of rejected background samples in earlier stages of the cascade. Our second approach proposes a novel optimization that performs a random filtering in the image to select a small number of detection windows, allowing a reduction in the computational cost. Our results show that accurate results can be achieved even when a large number of detection windows are discarded.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
The large number of surveillance cameras available nowadays in strategic points of major cities provides a safe environment. However, the huge amount of data provided by the cameras prevents its manual processing, requiring the application of automated methods. Among such methods, pedestrian detection plays an important role in reducing the amount of data by locating only the regions of interest for further processing regarding activities being performed by agents in the scene. However, the currently available methods are unable to process such large amount of data in real time. Therefore, there is a need for the development of optimization techniques. Towards accomplishing the goal of reducing costs for pedestrian detection, we propose in this work two optimization approaches. The first approach consists of a cascade of rejection based on Partial Least Squares (PLS) combined with the propagation of latent variables through the stages. Our results show that the method reduces the computational cost by increasing the number of rejected background samples in earlier stages of the cascade. Our second approach proposes a novel optimization that performs a random filtering in the image to select a small number of detection windows, allowing a reduction in the computational cost. Our results show that accurate results can be achieved even when a large number of detection windows are discarded. |
Antonio Carlos Nazare Junior; Cassio Elias Santos dos Junior; Renato Ferreira; William Robson Schwartz Smart Surveillance Framework: A Versatile Tool for Video Analysis Inproceedings Em: IEEE Winter Conference on Applications of Computer Vision, pp. 753–760, 2014. Links | BibTeX @inproceedings{wacv2014smart,
title = {Smart Surveillance Framework: A Versatile Tool for Video Analysis},
author = {Antonio Carlos Nazare Junior and Cassio Elias Santos dos Junior and Renato Ferreira and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2014-Smart-Surveillance-Framework-A-Versatile-Tool-for-Video-Analysis.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {IEEE Winter Conference on Applications of Computer Vision},
pages = {753--760},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Cassio Elias Santos dos Junior; William Robson Schwartz Extending Face Identification to Open-Set Face Recognition Inproceedings Em: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2014. Links | BibTeX @inproceedings{sibgrapi2014extending,
title = {Extending Face Identification to Open-Set Face Recognition},
author = {Cassio Elias Santos dos Junior and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2014-Extending-Face-Identification-to-Open-Set-Face-Recognition.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)},
pages = {1-8},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Cristianne Rodrigues Santos Dutra; M C Rocha; William Robson Schwartz Person Re-Identification Based on Weighted Indexing Structures Inproceedings Em: Iberoamerican Congress on Pattern Recognition (CIARP), pp. 359-366, Springer International Publishing, 2014. Links | BibTeX @inproceedings{Dutra:2014:CIARP,
title = {Person Re-Identification Based on Weighted Indexing Structures},
author = {Cristianne Rodrigues Santos Dutra and M C Rocha and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2014_CIARP_Dutra.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)},
volume = {8827},
pages = {359-366},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Gabriel Lorencetti Prado; William Robson Schwartz; Helio Pedrini Person Re-identification Using Partial Least Squares Appearance Modeling Incollection Em: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 382–390, 2013. Links | BibTeX @incollection{prado2013person,
title = {Person Re-identification Using Partial Least Squares Appearance Modeling},
author = {Gabriel Lorencetti Prado and William Robson Schwartz and Helio Pedrini},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2013-Person-Re-identification-Using-Partial-Least-Squares-Appearance-Modeling.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications},
pages = {382--390},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
|
G P Carlos; H Pedrini; William Robson Schwartz Fast and Scalable Enrollment for Face Identification based on Partial Least Squares Inproceedings Em: IEEE International Conference on Automatic Face and Gesture Recognition, 2013. Links | BibTeX @inproceedings{Carlos:2013:FG,
title = {Fast and Scalable Enrollment for Face Identification based on Partial Least Squares},
author = {G P Carlos and H Pedrini and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2013_FG.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {IEEE International Conference on Automatic Face and Gesture Recognition},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Victor Hugo Cunha de Melo; Samir Moreira Andrade Leao; M Campos; D Menotti; William Robson Schwartz Fast Pedestrian Detection based on a Partial Least Squares Cascade Inproceedings Em: IEEE International Conference on Image Processing, pp. 4146 - 4150, 2013. Links | BibTeX @inproceedings{Melo:2013:ICIPb,
title = {Fast Pedestrian Detection based on a Partial Least Squares Cascade},
author = {Victor Hugo Cunha de Melo and Samir Moreira Andrade Leao and M Campos and D Menotti and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2013-Fast-Pedestrian-Detection-based-on-a-Partial-Least-Squares-Cascade.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {IEEE International Conference on Image Processing},
pages = {4146 - 4150},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Cristianne Rodrigues Santos Dutra; T Souza; R Alves; William Robson Schwartz; L R Oliveira Re-identifying People based on Indexing Structure and Manifold Appearance Modeling Inproceedings Em: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 218-225, 2013. Links | BibTeX @inproceedings{Dutra:2013:SIBGRAPIb,
title = {Re-identifying People based on Indexing Structure and Manifold Appearance Modeling},
author = {Cristianne Rodrigues Santos Dutra and T Souza and R Alves and William Robson Schwartz and L R Oliveira},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/paper_2013_SIBGRAPI.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)},
pages = {218-225},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Raphael Felipe Carvalho de Prates; G Camara-Chavez; William Robson Schwartz; D Menotti Brazilian License Plate Detection Using Histogram of Oriented Gradients and Sliding Windows Journal Article Em: International Journal of Computer Science and Information Technology, 5 , pp. 39-52, 2013. Links | BibTeX @article{Prates:2013:IJCSIT,
title = {Brazilian License Plate Detection Using Histogram of Oriented Gradients and Sliding Windows},
author = {Raphael Felipe Carvalho de Prates and G Camara-Chavez and William Robson Schwartz and D Menotti},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2013_IJCSIT.pdf},
year = {2013},
date = {2013-01-01},
journal = {International Journal of Computer Science and Information Technology},
volume = {5},
pages = {39-52},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
