Alfredo Gardel

Unsupervised and adaptive Gaussian skin-color model

Abstract In this article a segmentation method is described for the face skin of people of any race in real time, in an adaptive and unsupervised way, based on a Gaussian model of the skin color (that will be referred to as Unsupervised and Adaptive Gaussian Skin-Color Model, UAGM). It is initialized by clustering and it is not required that the user introduces any initial parameters. It works with complex color images, with random backgrounds and it is robust to lighting and background changes. The clustering method used, based on the Vector Quantization (VQ) algorithm, is compared to other optimum model selection methods, based on the EM algorithm, using synthetic data. Finally, real results of the proposed method and conclusions are shown.

Directional People Counter Based on Head Tracking

This paper presents an application for counting people through a single fixed camera. This system performs the count distinction between input and output of people moving through the supervised area. The counter requires two steps: detection and tracking. The detection is based on finding peoples heads through preprocessed image correlation with several circular patterns. Tracking is made through the application of a Kalman filter to determine the trajectory of the candidates. Finally, the system updates the counters based on the direction of the trajectories. Different tests using a set of real video sequences taken from different indoor areas give results ranging between 87% and 98% accuracies depending on the volume of flow of people crossing the counting zone. Problematic situations, such as occlusions, people grouped in different ways, scene luminance changes, etc., were used to validate the performance of the system.

Implementation in Fpgas of Jacobi Method to Solve the Eigenvalue and Eigenvector Problem

This work shows a modular architecture based on FPGAs to solve the eigenvalue problem according to the Jacobi method. This method is able to solve the eigenvalues and eigenvectors concurrently. The main contribution of this work is the low execution time compared with other sequential algorithms, and minimal internal FPGA consumed resources, mainly due to the fact of using the CORDIC algorithm. Two CORDIC modules have been designed to solve the trigonometric operations involved. A parallel CORDIC architecture is proposed as it is the best option to compute the eigenvalues with this method. Both CORDIC modules can work in rotation and vector mode. The whole system has been done in VHDL language, attempting to optimize the design.

Commands generation by face movements applied to the guidance of a wheelchair for handicapped people

Describes a vision-based commands generation system, by face movements, applied to the guidance of an electric wheelchair for handicapped people with severe disabilities. Using a 2D color face tracker and a fuzzy detector the system computes face movements of the user and, depending on them, some commands are generated to drive the wheelchair. The system is non-intrusive and it allows visibility and freedom of head movements. It is able to learn the face movements of the user in an automatic initial setup, working even for people of different races. It is adaptive and, therefore, robust to light and background changes in inside environments. We report on some experimental results of this kind of guidance and some conclusions about its performance.

Novel HW Architecture Based on FPGAs Oriented to Solve the Eigen Problem

A hardware solution is presented to obtain the eigenvalues and eigenvectors of a real and symmetrical matrix using field-programmable gate arrays (FPGAs). Currently, this system is used to compute the eigenvalues and eigenvectors in covariance matrices for applications in digital image processing that make use of the principal component analysis (PCA) technique. The proposed solution in this paper is based on the Jacobi method, but in comparison with other related works, it presents a different architecture that remarkably improves execution time, while reducing the number of consumed resources of the FPGA.

Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis

Person re-identification is an open and challenging problem in computer vision. Existing re-identification approaches focus on optimal methods for features matching (e.g., metric learning approaches) or study the inter-camera transformations of such features. These methods hardly ever pay attention to the problem of visual ambiguities shared between the first ranks. In this paper, we focus on such a problem and introduce an unsupervised ranking optimization approach based on discriminant context information analysis. The proposed approach refines a given initial ranking by removing the visual ambiguities common to first ranks. This is achieved by analyzing their content and context information. Extensive experiments on three publicly available benchmark datasets and different baseline methods have been conducted. Results demonstrate a remarkable improvement in the first positions of the ranking. Regardless of the selected dataset, state-of-the-art methods are strongly outperformed by our method.

Guidance of a wheelchair for handicapped people by face tracking

This paper shows a guidance system for an electrical wheelchair for handicapped people by head movements. A color face tracking system has been developed in order to compute head movements of the user and, depending on them, some commands are generated to drive the wheelchair. The system is non-intrusive and it allows visibility and freedom of head movements. It is able to learn the face features of the user in an automatic initial setup, working even for people of different races. It is adaptive and, therefore, robust to light and background changes in inside environments. It has been tested with several users and some results are given.

Modeling feature distances by orientation driven classifiers for person re-identification

Display Omitted The person orientation is used to learn different inter-camera transformations.We propose a method to retrieve images of the person with different orientations.The pairwise feature dissimilarities space is used to create two regions according to the orientation.We train a binary classifier to capture the inter-camera transformation for each region. To tackle the re-identification challenges existing methods propose to directly match image features or to learn the transformation of features that undergoes between two cameras. Other methods learn optimal similarity measures. However, the performance of all these methods are strongly dependent from the person pose and orientation. We focus on this aspect and introduce three main contributions to the field: (i) to propose a method to extract multiple frames of the same person with different orientations in order to capture the complete person appearance; (ii) to learn the pairwise feature dissimilarities space (PFDS) formed by the subspaces of similar and different image pair orientations; and (iii) within each subspace, a classifier is trained to capture the multi-modal inter-camera transformation of pairwise image dissimilarities and to discriminate between positive and negative pairs. The experiments show the superior performance of the proposed approach with respect to state-of-the-art methods using two publicly available benchmark datasets.

An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices.

Person Orientation and Feature Distances Boost Re-identification

Most of the open challenges in person re-identification arise from the large variations of human appearance and from the different camera views that may be involved, making pure feature matching an unreliable solution. To tackle these challenges state-of-the-art methods assume that a unique inter-camera transformation of features undergoes between two cameras. However, the combination of view points, scene illumination and photometric settings, etc., together with the appearance, pose and orientation of a person make the inter-camera transformation of features multi-modal. To address these challenges we introduce three main contributions. We propose a method to extract multiple frames of the same person with different orientation. We learn the pair wise feature dissimilarities space (PFDS) formed by the subspace of pair wise feature dissimilarities computed between images of persons with similar orientation and the subspace of pair wise feature dissimilarities computed between images of persons non-similar orientations. Finally, a classifier is trained to capture the multi-modal inter-camera transformation of pair wise images for each subspace. To validate the proposed approach we show the superior performance of our approach to state-of-the-art methods using two publicly available benchmark datasets.