2016 |
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 | Tags: Smart Surveillance, Smart Surveillance Framework, SSF, Surveillance Systems, VER+, Video Surveillance @article{Nazare:2016:CVIU, title = {A scalable and flexible framework for smart video surveillance}, author = {Antonio Carlos Nazare Junior and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_CVIU.pdf}, year = {2016}, date = {2016-01-01}, journal = {Computer Vision and Image Understanding}, volume = {144}, number = {C}, pages = {258--275}, keywords = {Smart Surveillance, Smart Surveillance Framework, SSF, Surveillance Systems, VER+, Video Surveillance}, pubstate = {published}, tppubtype = {article} } |
2014 |
Junior, Antonio Carlos Nazare A Scalable and Versatile Framework for Smart Video Surveillance Masters Thesis Federal University of Minas Gerais, 2014. Resumo | Links | BibTeX | Tags: ARDOP, Smart Surveillance, Surveillance Systems, VER+, Video Surveillance @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 = {ARDOP, Smart Surveillance, Surveillance Systems, VER+, Video Surveillance}, 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 | Tags: ARDOP, Smart Surveillance, Smart Surveillance Framework, SmartView, SSF, Surveillance Systems, Video Surveillance @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 = {ARDOP, Smart Surveillance, Smart Surveillance Framework, SmartView, SSF, Surveillance Systems, Video Surveillance}, pubstate = {published}, tppubtype = {inproceedings} } |
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 | Tags: DET, Feature Extraction, Smart Surveillance, SmartView, Surveillance Systems, Video Surveillance @inproceedings{Nazare:2014:CIARP, title = {Scalable Feature Extraction for Visual Surveillance}, author = {Antonio Carlos Nazare Junior and Renato Ferreira and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2014_CIARP_Antonio.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, volume = {8827}, pages = {375-382}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, keywords = {DET, Feature Extraction, Smart Surveillance, SmartView, Surveillance Systems, Video Surveillance}, pubstate = {published}, tppubtype = {inproceedings} } |
2016 |
Antonio Carlos Nazare Junior; William Robson Schwartz A scalable and flexible framework for smart video surveillance Journal Article Computer Vision and Image Understanding, 144 (C), pp. 258–275, 2016. Links | BibTeX | Tags: Smart Surveillance, Smart Surveillance Framework, SSF, Surveillance Systems, VER+, Video Surveillance @article{Nazare:2016:CVIU, title = {A scalable and flexible framework for smart video surveillance}, author = {Antonio Carlos Nazare Junior and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_CVIU.pdf}, year = {2016}, date = {2016-01-01}, journal = {Computer Vision and Image Understanding}, volume = {144}, number = {C}, pages = {258--275}, keywords = {Smart Surveillance, Smart Surveillance Framework, SSF, Surveillance Systems, VER+, Video Surveillance}, pubstate = {published}, tppubtype = {article} } |
2014 |
Antonio Carlos Nazare Junior A Scalable and Versatile Framework for Smart Video Surveillance Masters Thesis Federal University of Minas Gerais, 2014. Resumo | Links | BibTeX | Tags: ARDOP, Smart Surveillance, Surveillance Systems, VER+, Video Surveillance @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 = {ARDOP, Smart Surveillance, Surveillance Systems, VER+, Video Surveillance}, 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. |
Antonio Carlos Nazare Junior; Cassio Elias Santos dos Junior; Renato Ferreira; William Robson Schwartz Smart Surveillance Framework: A Versatile Tool for Video Analysis Inproceedings IEEE Winter Conference on Applications of Computer Vision, pp. 753–760, 2014. Links | BibTeX | Tags: ARDOP, Smart Surveillance, Smart Surveillance Framework, SmartView, SSF, Surveillance Systems, Video Surveillance @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 = {ARDOP, Smart Surveillance, Smart Surveillance Framework, SmartView, SSF, Surveillance Systems, Video Surveillance}, pubstate = {published}, tppubtype = {inproceedings} } |
Antonio Carlos Nazare Junior; Renato Ferreira; William Robson Schwartz Scalable Feature Extraction for Visual Surveillance Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 375-382, Springer International Publishing, 2014. Links | BibTeX | Tags: DET, Feature Extraction, Smart Surveillance, SmartView, Surveillance Systems, Video Surveillance @inproceedings{Nazare:2014:CIARP, title = {Scalable Feature Extraction for Visual Surveillance}, author = {Antonio Carlos Nazare Junior and Renato Ferreira and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2014_CIARP_Antonio.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, volume = {8827}, pages = {375-382}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, keywords = {DET, Feature Extraction, Smart Surveillance, SmartView, Surveillance Systems, Video Surveillance}, pubstate = {published}, tppubtype = {inproceedings} } |