Andrews Cordolino Sobral

• Ph.D. on Computer Vision and Machine Learning
• Head of Artificial Intelligence at ActiveEon

Recent research on problem formulations based on decomposition into low-rank plus sparse matrices shows a suitable framework to separate moving objects from the background. The most representative problem formulation is the Robust Principal Component Analysis (RPCA) solved via Principal Component Pursuit (PCP) which decomposes a data matrix in a lo...

The BGSLibrary has been designed to provides an easy-to-use C++ framework and tools to perform background subtraction. First released in March 2012, currently the library offers 32 background subtraction algorithms. The source code is available under GNU GPL v3 license and the library is free for non-commercial use, open source and platform indepen...

The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used or adapted for other computer vision problems.

Designing a novel Local Binary Pattern (LBP) process usually relies heavily on human experts' knowledge and experience in the area. Even experts are often left with tedious episodes of trial and error until they identify an optimal LBP for a particular dataset. To address this problem, we present a novel symbolic regression able to automatically di...

Finding the best mathematical equation to deal with the different challenges found in complex scenarios requires a thorough understanding of the scenario and a trial and error process carried out by experts. In recent years, most state-of-the-art equation discovery methods have been widely applied in modeling and identification systems. However, eq...

This thesis introduces the recent advances on decomposition into low-rank plus sparse matrices and tensors, as well as the main contributions to face the principal issues in moving object detection. First, we present an overview of the state-of-the-art methods for low-rank and sparse decomposition, as well as their application to background modelin...

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approac...

Recent advances on low-rank and sparse decomposition for moving object detection Atelier: Enjeux dans la détection d’objets mobiles par soustraction de fond.

Background subtraction is an important task for visual surveillance systems. However, this task becomes more complex when the data size grows since the real-world scenario requires larger data to be processed in a more efficient way, and in some cases, in a continuous manner. Until now, most of background subtraction algorithms were designed for mo...

Background subtraction is an important task for visual surveillance systems. However, this task becomes more complex when the data size grows since the real-world scenario requires larger data to be processed in a more efficient way, and in some cases, in a continuous manner. Until now, most of background subtraction algorithms were designed for mo...

Background model initialization is commonly the first step of the background subtraction process. In practice, several challenges appear and perturb this process such as dynamic background, bootstrapping, illumination changes, noise image, etc. In this context, this work aims to investigate the background model initialization as a matrix completion...

Background model initialization is commonly the first step of the background subtraction process. In practice, several challenges appear and perturb this process such as dynamic background, bootstrapping, illumination changes, noise image, etc. In this context, this work aims to investigate the background model initialization as a matrix completion...

Background model initialization is commonly the first step of the background subtraction process. In practice, several challenges appear and perturb this process such as dynamic background, bootstrapping, illumination changes, noise image, etc. In this context, this work aims to investigate the background model initialization as a matrix completion...

The development of automated video-surveillance applications for maritime environment is a very difficult task due to the complexity of the scenes: moving water, waves, etc. The motion of the objects of interest (i.e. ships or boats) can be mixed with the dynamic behavior of the background (non-regular patterns). In this paper, a double-constrained...

The development of automated video-surveillance applications for maritime environment is a very difficult task due to the complexity of the scenes: moving water, waves, etc. The motion of the objects of interest (i.e. ships or boats) can be mixed with the dynamic behavior of the background (non-regular patterns). In this paper, a double-constrained...

Atualmente, sistemas de vídeo para monitoramento de tráfego urbano têm sido adotados com maior frequência. Através das imagens capturadas do trânsito, sistemas inteligentes baseados em visão computacional procuram extrair informações relevantes tais como a densidade, velocidade, localização e sentido dos veículos presentes na cena. Entretanto, as s...

Accurate and efficient foreground detection is an important task in video surveillance system. The task becomes more critical when the background scene shows more variations, such as water surface, waving trees, varying illumination conditions, etc. Recently, Robust Principal Components Analysis (RPCA) shows a very nice framework for moving object...

Background modeling and foreground object detection is the first step in visual surveillance system. The task becomes more difficult when the background scene contains significant variations, such as water surface, waving trees and sudden illumination conditions, etc. Recently, subspace learning model such as Robust Principal Component Analysis (RP...

Background subtraction (BS) is the art of separating moving objects from their background. The Background Modeling (BM) is one of the main steps of the BS process. Several subspace learning (SL) algorithms based on matrix and tensor tools have been used to perform the BM of the scenes. However, several SL algorithms work on a batch process increasi...

Background subtraction (BS) is the art of separating moving objects from their background. The Background Modeling (BM) is one of the main steps of the BS process. Several subspace learning (SL) algorithms based on matrix and tensor tools have been used to perform the BM of the scenes. However, several SL algorithms work on a batch process increasi...

Background subtraction (BS) is the art of separating moving objects from their background. The Background Modeling (BM) is one of the main steps of the BS process. Several subspace learning (SL) algorithms based on matrix and tensor tools have been used to perform the BM of the scenes. However, several SL algorithms work on a batch process increasi...

This work proposes a framework for facial expression recognition based on generalized procrustes analysis. The proposed system classifies seven different facial expressions: happiness, anger, sadness, surprise, disgust, fear and neutral. The proposed system was evaluated with the MUG Facial Expression database. Experimental results shows that the p...

This work proposes an automatic human-face expression recognition system that classifies seven different facial expressions: happiness, anger, sadness, surprise, disgust, fear and neutral. The experimental results show that the proposed system achieves the best hit hate using a linear discriminant classifier, 99.71% and 99.55% for MUG and FEEDTUM d...

Matrix and Tensor Tools for Computer Vision

The BGSLibrary provides a free easy-to-use C++ open source framework to perform background subtraction (BGS). Currently the library provides 29 BGS algorithms. In this work the library is described and the benchmark and performance evaluation of all BGS algorithms are shown. It is expected that the results presented here can help to choice the most...

This work proposes a holistic method for highway traffic video classification based on vehicle crowd properties. The method classifies the traffic congestion into three classes: light, medium and heavy. This is done by usage of average crowd density and crowd speed. Firstly, the crowd density is estimated by background subtraction and the crowd spe...

This work proposes a holistic method for highway traffic video classification based on vehicle crowd properties. The method classifies the traffic congestion into three classes: light, medium and heavy. This is done by usage of average crowd density and crowd speed. Firstly, the crowd density is estimated by background subtraction and the crowd spe...

A robótica é a ciência de perceber e manipular o ambiente real através de dispositivos mecânicos controlados por computador. Exemplos bem sucedidos de sistemas robóticos incluem plataformas móveis para a exploração planetária, braços robóticos nas linhas de montagem de indústrias, extração de petróleo em águas profundas, entre outros. Buscando esti...