VER+: Metodologias Robustas e Eficientes para Vigilância
Este projeto teve como foco principal o desenvolvimento e aprimoramento de técnicas de visão computacional para efetuar monitoramento de ambientes a partir de dados visuais obtidos por uma rede de câmeras de vigilância. Um dos principais objetivos do monitoramento automático de ambientes é a extração de informações a respeito de atividades desempenhadas pelos humanos de modo a detectar interações entre agentes e identificar padrões de comportamentos que sejam suspeitos. Este projeto visou desenvolver abordagens para resolução de problemas relacionados à vigilância de modo a reduzir o impacto causado pela propagação de erros ao longo da cadeia de problemas de interesse e aumentar a velocidade dos métodos.
Contribuições geradas à partir dessa pesquisa…
Carlos Antonio Caetano Junior; Jefersson A dos Santos; William Robson Schwartz
Em: IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016.
Rafael Henrique Vareto; Filipe Oliveira de Costa; William Robson Schwartz
Face Identification in Large Galleries Inproceedings
Em: Workshop on Face Processing Applications, pp. 1-4, 2016.
Raphael Felipe Carvalho de Prates; Cristianne Rodrigues Santos Dutra; William Robson Schwartz
Em: IEEE International Conference on Image Processing (ICIP), 2016.
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.
Artur Jordao; William Robson Schwartz
Em: IEEE International Conference on Image Processing (ICIP), pp. 2931-2935, 2016.
The Good, the Fast and the Better Pedestrian Detector Masters Thesis
Federal University of Minas Gerais, 2016.
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.
Antonio Carlos Nazare Junior; William Robson Schwartz
A scalable and flexible framework for smart video surveillance Journal Article
Em: Computer Vision and Image Understanding, 144 (C), pp. 258–275, 2016.
Cristianne Rodrigues Santos Dutra
Técnicas Otimizadas para Reidentificaçâo de Pessoas Masters Thesis
Federal University of Minas Gerais, 2016.
Cassio Elias Santos dos Junior
Partial Least Squares for Face Hashing Masters Thesis
Federal University of Minas Gerais, 2015.
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.
Cassio Elias Santos dos Junior; E Kijak; G Gravier; William Robson Schwartz
Learning to Hash Faces Using Large Feature Vectors Inproceedings
Em: Content-Based Multimedia Indexing (CBMI), 13th International Workshop on, pp. 1–6, IEEE, 2015.
Shuowen Hu; Jonghyun Choi; Alex L Chan; William Robson Schwartz
Thermal-to-visible Face Recognition using Partial Least Squares Journal Article
Em: Journal of the Optical Society of America A, 32 (3), pp. 431–442, 2015.
G L Prado; William Robson Schwartz; Helio Pedrini
Em: International Conference on Biometrics, pp. 1-7, 2015.
Gerson Paulo de Carlos; Helio Pedrini; William Robson Schwartz
Em: Journal of Visual Communication and Image Representation, 32 , pp. 170 - 179, 2015, ISSN: 1047-3203.
Raphael Felipe Carvalho de Prates; William Robson Schwartz
Em: IEEE International Conference on Image Processing (ICIP), pp. 1-5, 2015.
Antonio Carlos Nazare Junior
Federal University of Minas Gerais, 2014.
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.