Papers by Elias Herrero Jaraba
Análisis visual del movimiento humano
El trabajo presentado en esta memoria se centra basicamente en el estudio del analisis visual del... more El trabajo presentado en esta memoria se centra basicamente en el estudio del analisis visual del movimiento humano, y en el desarrollo de nuevas propuestas dentro de los distintos procesos integrantes de dicho analisis. En primer lugar, se presentan los objetivos inicialmente marcados, se ofrece una idea personal de lo que entendemos como analisis visual del movimiento humano, y se aportan las principales claves por las que dicho analisis puede convertirse en una herramienta de gran futuro. A continuacion, se comienza el trabajo propio de esta Tesis definiendo el proceso de deteccion de movimiento propuesto. Este proceso se ha disenado bajo las siguientes condiciones: debe ser un sistema lo mas general posible y debe ser robusto frente a cambios de iluminacion de distinto tipo. Siguiendo con la presentacion del resto de los procesos, se continua con el desarrollo de los metodos de segmentacion de la figura humana. Se han disenado tres tipos independientes de segmentacion. El primer...
IEEE Access
Today, the classification and location of faults in electrical networks remains a topic of great ... more Today, the classification and location of faults in electrical networks remains a topic of great interest. Faults are a major issue, mainly due to the time spent to detect, locate, and repair the cause of the fault. To reduce time and associated costs, automatic fault classification and location is gaining great interest. State-of-the-art techniques to classify and locate faults are mainly based on line-impedance measurements or the detection of the traveling wave produced by the event caused by the fault itself. In contrast, this paper describes the methodology for creating a database and a model for a complex distribution network. Both objectives are covered under the paradigm of the time-domain pulse reflectometry (TDR) principle. By using this technique, large distances can be monitored on a line with a single device. Thus, in this way a database is shared and created from the results of simulations of a real and complex distribution network modeled in the PSCAD TM software, which have been validated with measurements from an experimental test setup. Experimental validations have shown that the combination of the TDR technique with the modeling of a real network (including the real injector and the network coupling filter from the prototype) provides high-quality signals that are very similar and reliable to the real ones. In this sense, it is intended firstly that this model and its corresponding data will serve as a basis for further processing by any of the existing state-of-the-art techniques. And secondly, to become a valid alternative to the already well-known Test Feeders but adapted to work groups not used to the electrical world but to the environment of pure data processing.
2009 Advanced Video and Signal Based Surveillance
IEEE Access
This study proposes a new method for detecting and classifying faults in distribution lines. The ... more This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCAD TM). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate. INDEX TERMS Artificial Neural Networks (ANNs), deep learning, siamese networks, generative adversarial neural networks (GAN's), fault classification, fault detection, transmission lines.
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009
This paper addresses the problem of silhouettebased human action modelling and recognition, speci... more This paper addresses the problem of silhouettebased human action modelling and recognition, specially when the number of action samples is scarce. The first step of the proposed system is the 2D modelling of human actions based on motion templates, by means of Motion History Images (MHI). These templates are projected into a new subspace using the Kohonen Self Organizing feature Map (SOM), which groups viewpoint (spatial) and movement (temporal) in a principal manifold, and models the high dimensional space of static templates.The next step is based on the Hidden Markov Models (HMM) in order to track the map behavior on the temporal sequences of MHI. Every new MHI pattern is compared with the features map obtained during the training. The index of the winner neuron is considered as discrete observation for the HMM. If the number of samples is not enough, a sampling technique, the Sampling Importance Resampling (SIR) algorithm, is applied in order to increase the number of observations for the HMM. Finally, temporal pattern recognition is accomplished by a Maximum Likelihood (ML) classifier. We demonstrate this approach on two publiclyavailable dataset: one based on real actors and another one based on virtual actors.
Human Recognition by Gait Analysis Using Neural Networks
Lecture Notes in Computer Science, 2002
This paper presents a new method to recognize people by their gait, which forms part of a major p... more This paper presents a new method to recognize people by their gait, which forms part of a major project to detect and recognize automatically different human behaviours. The project is comprised of several stages, but this paper is focused on the last one, i.e. recognition. This stage is based on the Self-Organizing Map, assuming that the previous stage of feature
Video-based human posture recognition
Proceedings of the 2004 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2004. CIHSPS 2004., 2004
We propose a human behaviour recognition system based on video sequences. Our aim is to identify ... more We propose a human behaviour recognition system based on video sequences. Our aim is to identify one among several kinds of actions performed by a single person in a particular scenery. Each frame will be processed, detecting the moving objects and using a new statistical-based algorithm to erase shadows. The final step consists of the extraction of different kinds of
Journal of Mathematical Imaging and Vision, 2010
In this paper, we show how interacting and occluding targets can be tackled successfully within a... more In this paper, we show how interacting and occluding targets can be tackled successfully within a Gaussian approximation. For that purpose, we develop a general expansion of the mean and covariance of the posterior and we consider a first order approximation of it. The proposed method differs from EKF in that neither a non-linear dynamical model nor a non-linear measurement vector to state relation have to be defined, so it works with any kind of interaction potential and likelihood. The approach has been tested on three sequences (10400, 2500, and 400 frames each one). The results show that our approach helps to reduce the number of failures without increasing too much the computation time with respect to methods that do not take into account target interactions.
Automatic Recognition System of Human Activities
Erratum to "Shape matching of partially occluded curves invariant under projective transformation
Computer Vision and Image Understanding, 2004
This paper presents a geometric measure that can be used to gauge the similarity of 2D shapes by ... more This paper presents a geometric measure that can be used to gauge the similarity of 2D shapes by comparing their skeletons. The measure is defined to be the rate of change of boundary length with distance along the skeleton. We demonstrate that this...
The classification problem of determining if a surveillance camera sees persons is tackled with t... more The classification problem of determining if a surveillance camera sees persons is tackled with two neural models: the Self-Organizing Map (SOM) with supervision as in a classical conditioning analogy and Multi Layer Perceptrons (MLP). The first model, that we call Conditioning-SOM (C-SOM) allowed a quick selection of input features with a good tradeoff between computational cost and classification performance. Finally, MLP classifiers were trained with the selected features. The classification performance of both neural models was very good with very simple features.
In This work we propose a statistical model for detection and tracking of human silhouette and th... more In This work we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a principal component analysis (PCA). The problem of non-linear PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model helps increase the reliability and robustness.
Computer Vision and Image Understanding, 2009
In this paper, we consider the problem of tracking similar objects. We show how a mean field appr... more In this paper, we consider the problem of tracking similar objects. We show how a mean field approach can be used to deal with interacting targets and we compare it with Markov Chain Monte Carlo (MCMC). Two mean field implementations are presented. The first one is more general and uses particle filtering. We discuss some simplifications of the base algorithm that reduce the computation time. The second one is based on suitable Gaussian approximations of probability densities that lead to a set of self-consistent equations for the means and covariances. These equations give the Kalman solution if there is no interaction. Experiments have been performed on two kinds of sequences. The first kind is composed of a single long sequence of twenty roaming ants and was previously analysed using MCMC. In this case, our mean field algorithms obtain substantially better results. The second kind corresponds to selected sequences of a football match in which the interaction avoids tracker coalescence in situations where independent trackers fail.
Automatic detection and classification of football players
ABSTRACT Color segmentation of images usually requires a manual selection and classification of s... more ABSTRACT Color segmentation of images usually requires a manual selection and classification of samples to train the system. This paper presents an automatic system that performs these tasks without the need of a long training, providing a useful tool to detect and identify figures. In real situations, it is necessary to repeat the training process if light conditions change, or if, in the same scenario, the colors of the figures and the background may have changed, being useful a fast training method. A direct application of this method is the detection and identification of football players.
Multicamera sport player tracking with Bayesian estimation of measurements
Optical Engineering, 2009
We propose a complete application capable of tracking multiple objects in an environment monitore... more We propose a complete application capable of tracking multiple objects in an environment monitored by multiple cameras. The system has been specially developed to be applied to sport games, and it has been evaluated in a real association-football stadium. Each target is tracked using a local importance-sampling particle filter in each camera, but the final estimation is made by combining information from the other cameras using a modified unscented Kalman filter algorithm. Multicamera integration enables us to compensate for bad measurements or occlusions in some cameras thanks to the other views it offers. The final algorithm results in a more accurate system with a lower failure rate.
Computer Vision and Image Understanding, 2004
This paper describes a method to identify partially occluded shapes which are randomly oriented i... more This paper describes a method to identify partially occluded shapes which are randomly oriented in 3D space. The goal is to match the object contour present in an image with an object in a database. The approach followed is the alignment method which has been described in detail in the literature. Using this approach the recognition process is divided into two stages: first, the transformation between the viewed object and the model object is determined, and second, the model that best matches the viewed object is found. In the first stage, invariant points under projective transformation (based on bitangency) are used, which drastically reduced the selection space for alignment. Next, the curves are compared after the transformation matrix is estimated between the image and the model in order to determine the pose of the curve that undergoes the perspective projection. The evaluation process is performed using a novel estimation of the Hausdorff distance (HD), called the continuity HD. It evaluates partially occluded curves in the image in relation to the complete contour in the database. The experimental results showed that the present algorithm can cope with noisy figures, projective transformations, and complex occlusions.
Visual Tracking on the Ground - A Comparative Analysis
Applied Catalysis A-general, 1999
Pillared clays with Al and AlGa polyoxycations were prepared and characterized in order to obtain... more Pillared clays with Al and AlGa polyoxycations were prepared and characterized in order to obtain materials suitable as active components in catalysts for heavy oil cracking, evaluated using a microactivity test (MAT). DRX measurements and textural parameters at different calcination temperatures showed the highest thermal stability for the AlGa-PILC sample (d 0 0 1 17.3 A Ê ; S BET and V micropores remained around 85% at 7008C with respect to the pillared material). The AlGa sample also showed higher gas oil cracking conversion and ole®n production than the Al-PILC sample. #
Detected motion classification with a double-background and a Neighborhood-based difference
Pattern Recognition Letters, 2003
This paper describes a new method to detect moving objects in a dynamic scene based on background... more This paper describes a new method to detect moving objects in a dynamic scene based on background subtraction. The main goal of the method is to obtain and keep a stable background image to cope with variations on environmental changing conditions. In this way, we use a double background (long-term background and short-term background) to deal with temporal stability and fast changes. In addition, this method computes the temporal changes in the video sequence by a local convolution mask taking into account the information of the pixel neighborhood, being less sensitive to noise. Besides, the method classifies the regions of change in moving and static blobs. The first ones rep- resent real moving objects, and the second are related to illumination changes and noise. Finally, experimental results and a performance measure establishing the confidence of the method are presented. � 2003 Elsevier Science B.V. All rights reserved.
In this paper we present a complete chain of algorithms for detection and tracking of moving obje... more In this paper we present a complete chain of algorithms for detection and tracking of moving objects using a static camera. The system is based on robust difference of images for motion detection. However, the difference of images does not take place directly over the image frames, but over two robust frames which are continuously constructed by temporal median filtering on a set of last grabbed images, which allows working with slow illumination changes. The system also includes a Kalman filter for tracking objects, which is also employed in two ways: assisting to the process of object detection and providing the object state that models its behaviour. These algorithms have given us a more robust method of detection, making possible the handling of occlusions as can be seen in the experimentation made with outdoor traffic scenes.
Uploads
Papers by Elias Herrero Jaraba