Antônio Carlos Nazaré Júnior

Ph.D. Student

Lattes: http://lattes.cnpq.br/5119182011331334
Email: acnazarejr@gmail.com

Master’s Degree in Computer Science from the Federal University of Minas Gerais (2014) and a bachelor’s degree in Computer Science from the Federal University of Ouro Preto (2011). He is currently a technological development fellow at CNPQ. He has experience in the area of Computer Science, with emphasis on high performance computing applied to computer vision, working mainly on the following topics: Intelligent Surveillance Systems, Video Analysis, Parallel and Distributed Computing, Programming in SIMD architectures.

Master’s Thesis

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Publications

Jordao, Artur; Junior, Antonio Carlos Nazare; Sena, Jessica; Schwartz, William Robson

Human Activity Recognition based on Wearable Sensor Data: A Benchmark Journal Article

arXiv, pp. 1-12, 2018.

Links | BibTeX

Junior, Antonio Carlos Nazare; de Costa, Filipe Oliveira; Schwartz, William Robson

Content-Based Multi-Camera Video Alignment using Accelerometer Data Inproceedings

Advanced Video and Signal Based Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 1-6, 2018.

Links | BibTeX

Junior, Antonio Carlos Nazare; Schwartz, William Robson

A scalable and flexible framework for smart video surveillance Journal Article

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

Links | BibTeX

Junior, Antonio Carlos Nazare

A Scalable and Versatile Framework for Smart Video Surveillance Masters Thesis

Federal University of Minas Gerais, 2014.

Abstract | 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.

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

Smart Surveillance Framework: A Versatile Tool for Video Analysis Inproceedings

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

Links | BibTeX

Junior, Antonio Carlos Nazare; Ferreira, Renato; Schwartz, William Robson

Scalable Feature Extraction for Visual Surveillance Inproceedings

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

Links | BibTeX