Persistent homology-based gait recognition robust to upper body variations

Gait recognition is nowadays an important biometric technique for video surveillance tasks, due to the advantage of using it at distance. However, when the upper body movements are unrelated to the natural dynamic of the gait, caused for example by carrying a bag or wearing a coat, the reported results show low accuracy. With the goal of solving this problem, we apply persistent homology to extract topological features from the lowest fourth part of the body silhouettes. To obtain the features, we modify our previous algorithm for gait recognition, to improve its efficacy and robustness to variations in the amount of simplices of the gait complex. We evaluate our approach using the CASIA-B dataset, obtaining a considerable accuracy improvement of 93:8%, achieving at the same time invariance to upper body movements unrelated with the dynamic of the gait.Ministerio de Economía y Competitividad MTM2015-67072-

Topological signature for periodic motion recognition

In this paper, we present an algorithm that computes the topological signature for a given periodic motion sequence. Such signature consists of a vector obtained by persistent homology which captures the topological and geometric changes of the object that models the motion. Two topological signatures are compared simply by the angle between the corresponding vectors. With respect to gait recognition, we have tested our method using only the lowest fourth part of the body's silhouette. In this way, the impact of variations in the upper part of the body, which are very frequent in real scenarios, decreases considerably. We have also tested our method using other periodic motions such as running or jumping. Finally, we formally prove that our method is robust to small perturbations in the input data and does not depend on the number of periods contained in the periodic motion sequence.Comment: arXiv admin note: substantial text overlap with arXiv:1707.0698

Persistent-homology-based gait recognition

Gait recognition is an important biometric technique for video surveillance tasks, due to the advantage of using it at distance. In this paper, we present a persistent homology-based method to extract topological features (the so-called topological gait signature) from the the body silhouettes of a gait sequence. It has been used before in sev- eral conference papers of the same authors for human identi cation, gender classi cation, carried object detection and monitoring human activities at distance. The novelty of this paper is the study of the sta- bility of the topological gait signature under small perturbations and the number of gait cycles contained in a gait sequence. In other words, we show that the topological gait signature is robust to the presence of noise in the body silhouettes and to the number of gait cycles con- tained in a given gait sequence. We also show that computing our topological gait signature of only the lowest fourth part of the body silhouette, we avoid the upper body movements that are unrelated to the natural dynamic of the gait, caused for example by carrying a bag or wearing a coat.Ministerio de Economía y Competitividad MTM2015-67072-

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like scene monitoring over time or the scene analysis after sudden events. These tasks often require the fusion of geo-referenced and precisely co-registered multi-sensor data. Images captured by high resolution synthetic aperture radar (SAR) satellites have an absolute geo-location accuracy within few decimeters. This renders SAR images interesting as a source for the geo-location improvement of optical images, whose geo-location accuracy is in the range of some meters. In this paper, we are investigating a deep learning based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data. Image registration between SAR and optical satellite images requires few but accurate and reliable matching points. To derive such matching points a neural network based on a Siamese network architecture was trained to learn the two dimensional spatial shift between optical and SAR image patches. The neural network was trained over TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The results of the proposed method confirm that accurate and reliable matching points are generated with a higher matching accuracy and precision than state-of-the-art approaches

Computational methods for percussion music analysis : the afro-uruguayan candombe drumming as a case study

Most of the research conducted on information technologies applied to music has been largely limited to a few mainstream styles of the so-called `Western' music. The resulting tools often do not generalize properly or cannot be easily extended to other music traditions. So, culture-specific approaches have been recently proposed as a way to build richer and more general computational models for music. This thesis work aims at contributing to the computer-aided study of rhythm, with the focus on percussion music and in the search of appropriate solutions from a culture specifc perspective by considering the Afro-Uruguayan candombe drumming as a case study. This is mainly motivated by its challenging rhythmic characteristics, troublesome for most of the existing analysis methods. In this way, it attempts to push ahead the boundaries of current music technologies. The thesis o ers an overview of the historical, social and cultural context in which candombe drumming is embedded, along with a description of the rhythm. One of the specific contributions of the thesis is the creation of annotated datasets of candombe drumming suitable for computational rhythm analysis. Performances were purposely recorded, and received annotations of metrical information, location of onsets, and sections. A dataset of annotated recordings for beat and downbeat tracking was publicly released, and an audio-visual dataset of performances was obtained, which serves both documentary and research purposes. Part of the dissertation focused on the discovery and analysis of rhythmic patterns from audio recordings. A representation in the form of a map of rhythmic patterns based on spectral features was devised. The type of analyses that can be conducted with the proposed methods is illustrated with some experiments. The dissertation also systematically approached (to the best of our knowledge, for the first time) the study and characterization of the micro-rhythmical properties of candombe drumming. The ndings suggest that micro-timing is a structural component of the rhythm, producing a sort of characteristic "swing". The rest of the dissertation was devoted to the automatic inference and tracking of the metric structure from audio recordings. A supervised Bayesian scheme for rhythmic pattern tracking was proposed, of which a software implementation was publicly released. The results give additional evidence of the generalizability of the Bayesian approach to complex rhythms from diferent music traditions. Finally, the downbeat detection task was formulated as a data compression problem. This resulted in a novel method that proved to be e ective for a large part of the dataset and opens up some interesting threads for future research.La mayoría de la investigación realizada en tecnologías de la información aplicadas a la música se ha limitado en gran medida a algunos estilos particulares de la así llamada música `occidental'. Las herramientas resultantes a menudo no generalizan adecuadamente o no se pueden extender fácilmente a otras tradiciones musicales. Por lo tanto, recientemente se han propuesto enfoques culturalmente específicos como forma de construir modelos computacionales más ricos y más generales. Esta tesis tiene como objetivo contribuir al estudio del ritmo asistido por computadora, desde una perspectiva cultural específica, considerando el candombe Afro-Uruguayo como caso de estudio. Esto está motivado principalmente por sus características rítmicas, problemáticas para la mayoría de los métodos de análisis existentes. Así , intenta superar los límites actuales de estas tecnologías. La tesis ofrece una visión general del contexto histórico, social y cultural en el que el candombe está integrado, junto con una descripción de su ritmo. Una de las contribuciones específicas de la tesis es la creación de conjuntos de datos adecuados para el análisis computacional del ritmo. Se llevaron adelante sesiones de grabación y se generaron anotaciones de información métrica, ubicación de eventos y secciones. Se disponibilizó públicamente un conjunto de grabaciones anotadas para el seguimiento de pulso e inicio de compás, y se generó un registro audiovisual que sirve tanto para fines documentales como de investigación. Parte de la tesis se centró en descubrir y analizar patrones rítmicos a partir de grabaciones de audio. Se diseñó una representación en forma de mapa de patrones rítmicos basada en características espectrales. El tipo de análisis que se puede realizar con los métodos propuestos se ilustra con algunos experimentos. La tesis también abordó de forma sistemática (y por primera vez) el estudio y la caracterización de las propiedades micro rítmicas del candombe. Los resultados sugieren que las micro desviaciones temporales son un componente estructural del ritmo, dando lugar a una especie de "swing" característico. El resto de la tesis se dedicó a la inferencia automática de la estructura métrica a partir de grabaciones de audio. Se propuso un esquema Bayesiano supervisado para el seguimiento de patrones rítmicos, del cual se disponibilizó públicamente una implementación de software. Los resultados dan evidencia adicional de la capacidad de generalización del enfoque Bayesiano a ritmos complejos. Por último, la detección de inicio de compás se formuló como un problema de compresión de datos. Esto resultó en un método novedoso que demostró ser efectivo para una buena parte de los datos y abre varias líneas de investigación

Maximum entropy-based modeling of community-level hazard responses for civil infrastructures

Perturbed by natural hazards, community-level infrastructure networks operate like many-body systems, with behaviors emerging from coupling individual component dynamics with group correlations and interactions. It follows that we can borrow methods from statistical physics to study the response of infrastructure systems to natural disasters. This study aims to construct a joint probability distribution model to describe the post-hazard state of infrastructure networks and propose an efficient surrogate model of the joint distribution for large-scale systems. Specifically, we present maximum entropy modeling of the regional impact of natural hazards on civil infrastructures. Provided with the current state of knowledge, the principle of maximum entropy yields the ``most unbiased`` joint distribution model for the performances of infrastructures. In the general form, the model can handle multivariate performance states and higher-order correlations. In a particular yet typical scenario of binary performance state variables with knowledge of their mean and pairwise correlation, the joint distribution reduces to the Ising model in statistical physics. In this context, we propose using a dichotomized Gaussian model as an efficient surrogate for the maximum entropy model, facilitating the application to large systems. Using the proposed method, we investigate the seismic collective behavior of a large-scale road network (with 8,694 nodes and 26,964 links) in San Francisco, showcasing the non-trivial collective behaviors of infrastructure systems