@inproceedings{Caetano:2016:ICPR,
title = {Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor},
author = {Carlos Antonio Caetano Junior and Jefersson A dos Santos and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/ICPR2016.pdf},
year = {2016},
date = {2016-12-13},
booktitle = {IAPR International Conference on Pattern Recognition (ICPR)},
pages = {1-6},
keywords = {Activity Recognition, Descriptor, Feature Extraction, Featured Publication, OFCM, VER+},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Vareto:2016:WFPA,
title = {Face Identification in Large Galleries},
author = {Rafael Henrique Vareto and Filipe Oliveira de Costa and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_WFPA.pdf},
year = {2016},
date = {2016-10-02},
booktitle = {Workshop on Face Processing Applications},
pages = {1-4},
keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, VER+},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Prates2016ICIP,
title = {Predominant Color Name Indexing Structure for Person Re-Identification},
author = {Raphael Felipe Carvalho de Prates and Cristianne Rodrigues Santos Dutra and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_ICIP_Prates.pdf},
year = {2016},
date = {2016-09-25},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
abstract = {The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery.},
keywords = {GigaFrames, Person Re-Identification, VER+},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Correia:2016:ICIP,
title = {Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection},
author = {Artur Jordao and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/OBLIQUE-RANDOM-FOREST-BASED-ON-PARTIAL-LEAST-SQUARES-APPLIED-TO.pdf},
year = {2016},
date = {2016-09-25},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
pages = {2931-2935},
keywords = {DeepEyes, Pedestrian Detection, VER+},
pubstate = {published},
tppubtype = {inproceedings}
}

@mastersthesis{Jordao:2016:MSc,
title = {The Good, the Fast and the Better Pedestrian Detector},
author = {Artur Jordao},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/dissertation_2016_ArturJordao.pdf},
year = {2016},
date = {2016-06-24},
school = {Federal University of Minas Gerais},
abstract = {Pedestrian detection is a well-known problem in Computer Vision, mostly because of its direct applications in surveillance, transit safety and robotics. In the past decade, several efforts have been performed to improve the detection in terms of accuracy, speed and feature enhancement. In this work, we propose and analyze techniques focusing on these points. First, we develop an accurate oblique random forest (oRF) associated with Partial Least Squares (PLS). The method utilizes the PLS to find a decision surface, at each node of a decision tree, that better splits the samples presented to it, based on some purity criterion. To measure the advantages provided by PLS on the oRF, we compare the proposed method with the oRF based on SVM. Second, we evaluate and compare filtering approaches to reduce the search space and keep only potential regions of interest to be presented to detectors, speeding up the detection process. Experimental results demonstrate that the evaluated filters are able to discard a large number of detection windows without compromising the accuracy. Finally, we propose a novel approach to extract powerful features regarding the scene. The method combines results of distinct pedestrian detectors by reinforcing the human hypothesis, whereas suppressing a significant number of false positives due to the lack of spatial consensus when multiple detectors are considered. Our proposed approach, referred to as Spatial Consensus (SC), outperforms all previously published state-of-the-art pedestrian detection methods.},
keywords = {DeepEyes, DET, GigaFrames, Pedestrian Detection, VER+},
pubstate = {published},
tppubtype = {mastersthesis}
}

@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}
}

@mastersthesis{Dutra:2016:MSc,
title = {Técnicas Otimizadas para Reidentificaçâo de Pessoas},
author = {Cristianne Rodrigues Santos Dutra},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/thesis_2016_Cristianne.pdf},
year = {2016},
date = {2016-01-01},
school = {Federal University of Minas Gerais},
keywords = {DeepEyes, GigaFrames, Person Re-Identification, VER+},
pubstate = {published},
tppubtype = {mastersthesis}
}

@mastersthesis{Santos:2015:MSc,
title = {Partial Least Squares for Face Hashing},
author = {Cassio Elias Santos dos Junior},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/dissertation_2015_Cassio.pdf},
year = {2015},
date = {2015-08-24},
school = {Federal University of Minas Gerais},
abstract = {Face identification is an important research topic due to areas such as its application to surveillance, forensics and human-computer interaction. In the past few years, a myriad of methods for face identification has been proposed in the literature, with just a few among them focusing on scalability. In this work, we propose a simple but efficient approach for scalable face identification based on partial least squares (PLS) and random independent hash functions inspired by locality-sensitive hashing (LSH), resulting in the PLS for hashing (PLSH) approach. The original PLSH approach is further extended using feature selection to reduce the computational cost to evaluate the PLS-based hash functions, resulting in the state-of-the-art extended PLSH approach (ePLSH). The proposed approach is evaluated in the dataset FERET and in the dataset FRGCv1. The results show significant reduction in the number of subjects evaluated in the face identification (reduced to 0.3% of the gallery), providing averaged speedups up to 233 times compared to evaluating all subjects in the face gallery and 58 times compared to previous works in the literature.},
keywords = {DeepEyes, Face Identification, Face Recognition, GigaFrames, Indexing Structure, Local Sensitive Hashing, Partial Least Squares, VER+},
pubstate = {published},
tppubtype = {mastersthesis}
}

@inproceedings{santos2015learning,
title = {Learning to Hash Faces Using Large Feature Vectors},
author = {Cassio Elias Santos dos Junior and E Kijak and G Gravier and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2015-Learning_to_Hash_Faces_Using_Large_Feature_Vectors.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Content-Based Multimedia Indexing (CBMI), 13th International Workshop on},
pages = {1--6},
publisher = {IEEE},
keywords = {Face Identification, Face Recognition, GigaFrames, Indexing Structure, Locality Sensitive Hashing, Partial Least Squares, SmartView, VER+},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{Hu:2015:JOSAA,
title = {Thermal-to-visible Face Recognition using Partial Least Squares},
author = {Shuowen Hu and Jonghyun Choi and Alex L Chan and William Robson Schwartz},
url = {http://dx.doi.org/10.1364/JOSAA.32.000431},
year = {2015},
date = {2015-01-01},
journal = {Journal of the Optical Society of America A},
volume = {32},
number = {3},
pages = {431--442},
publisher = {OSA},
keywords = {Face Recognition, Thermal Imaging, VER+},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{Prado:2015:ICB,
title = {A Verify-Correct Approach to Person Re-identification Based on Partial Least Squares Signatures},
author = {G L Prado and William Robson Schwartz and Helio Pedrini},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_ICB_Prado.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {International Conference on Biometrics},
pages = {1-7},
series = {Lecture Notes in Computer Science},
keywords = {Partial Least Squares, Person Re-Identification, SmartView, VER+},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{2015:JVCI:Carlos,
title = {Classification schemes based on Partial Least Squares for face identification},
author = {Gerson Paulo de Carlos and Helio Pedrini and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_JVCI.pdf},
issn = {1047-3203},
year = {2015},
date = {2015-01-01},
journal = {Journal of Visual Communication and Image Representation},
volume = {32},
pages = {170 - 179},
keywords = {Face Identification, Face Recognition, One-Against-All Classification Scheme, Partial Least Squares, VER+},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{Prates:2015:ICB,
title = {CBRA: Color-Based Ranking Aggregation for Person Re-Identification},
author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_ICIP_Prates.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
pages = {1-5},
keywords = {CBRA, GigaFrames, Person Re-Identification, Ranking Aggregation, SmartView, VER+},
pubstate = {published},
tppubtype = {inproceedings}
}

@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}
}