Understanding Target Trajectory Behavior: A Dynamic Scene Modeling Approach

[Resumen] El análisis de comportamiento humano es uno de los campos más activos en la rama de visión por computador. Con el incremento de cámaras, especialmente en entornos controlados tales como aeropuertos, estaciones de tren o museos, se hace cada vez más necesario el uso de sistemas automáticos que puedan catalogar la información proporcionada. En el caso de entornos concurridos, es muy difícil el poder distinguir el comportamiento de personas en base a sus gestos, debido a la falta de visión de su cuerpo al completo. Por ende, el análisis de comportamiento se realiza en base a sus trayectorias, añadiendo técnicas de razonamiento de alto nivel para ulilizar dicha información en múltiples aplicaciones, tales como la video vigilancia o el análisis de tráfico. El propósito de esta investigación es el desarrollo de un sistema totalmente automático para el análisis de comportamiento de las personas. Por una parte, se presentan dos sistemas para el seguimiento de múltiples objetivos, así como un sistema novedoso para la re-identificación de personas, con la intención de detectar todo objeto de interés en la escena, devolviendo sus trayectorias como salida. Por otra parte, se presenta un sistema novedoso para el análisis de comportamiento basado en información del entorno de la escena. Está basado en la idea que que toda persona,cuando intenta llegar a un cierto lugar, tiende a seguir el mismo camino que suele utilizar la mayoría de la gente. Se presentan una serie de métricas para la detección de movimientos anómalos, haciendo que este método sea ideal para su utilización en sistemas de tiempo real.[Abstract] Human behavior analysis is one of the most active computer vision research fields. As the number of cameras are increased, especially in restricted environments, like airports, train stations or museums, the need of automatic systems that can catalog the information provided by the cameras becomes crucial. In the case of crowded scenes, it is very difficult to distinguish people behavior because of the lack of visual contact of the whole body. Thus, behavior analysis remains in the evaluation of trajectories, adding high-level knowledge approaches in order to use that information in several applications like video surveillance or traffic analysis. The proposal of this research is the design of a fully-automatic human behavior system from a distance. On the one hand, two different multiple-target tracking methods and a target re-identification procedure are presented to detect every target in the scene, returning their trajectories as output. On the other hand, a novel behavior analysis system, which includes information about the environment, is provided. It is based in the idea that every person tries to reach a goal in the scene following the same path the majority of people should use. An extremely fast abnormal behavior metric is presented, providing our method with the capabilities needed to be used in real-time scenarios[Resumo] A análise do comportamento humano é un dos campos máis activos na rama da visión por computadora. Co incremento de cámaras, especialmente en entornos controlados tales coma aeroportos, estacións de tren ou museos, faise cada vez máis necesario o uso de sistemas automáticos que poidan catalogar a información proporcionada. No caso de entornos concurridos, é moi complicado de poder distinguir o comportamento de persoas dacordo cos seus xestos, debido á falta dunha visión completa do corpo do suxeito. Por tanto, a análise de comportamento tende a realizarse en base á traxectoria, engadindo técnicas de razoamento de alto nivel para utilizar dita información en diversas aplicacións, tales coma a video vixiancia ou a análise de tráfico. O propósito desta investigación é o desenrolo dun sistema totalmente automático para a análise do comportamento das persoas. Por unha parte, preséntanse dous sistemas para o seguimento de múltiples obxectivos, así coma un sistema novidoso para a re-identificación de persoas, coa intención de detectar todo obxecto de interés na escena, devolvendo as traxectorias asociadas como saída. Por outra parte, preséntase un sistema novidoso para a análise de comportamente baseada na informaci ón do entorno da escena. Está baseado na idea de que toda persoa, cando intenta acadar un certo luegar, tende a seguir o mesmo cami~no que xeralmente usa a maioría da xente. Preséntanse unha serie de métricas para a detección de movementos anómalos, facendo posible que este método poida ser utilizado en sistemas de tempo real

Wavefront Marching Methods: A Unified Algorithm to Solve Eikonal and Static Hamilton-Jacobi Equations

© 2020 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/TPAMI.2020.2993500[Abstract]: This paper presents a unified propagation method for dealing with both the classic Eikonal equation, where the motion direction does not affect the propagation, and the more general static Hamilton-Jacobi equations, where it does. While classic Fast Marching Method (FMM) techniques achieve the solution to the Eikonal equation with a O(M log M) (or O(M) assuming some modifications), solving the more general static Hamilton-Jacobi equation requires a higher complexity. The proposed framework maintains the O(M log M) complexity for both problems, while achieving higher accuracy than available state-of-the-art. The key idea behind the proposed method is the creation of ‘mini wave-fronts’, where the solution is interpolated to minimize the discretization error. Experimental results show how our algorithm can outperform the state-of-the-art both in precision and computational cost.The authors would like to thank to the financial support of the Spanish Ministerio de Economıa y Competitividad (research project TIN2015-65069-C2-1-R), the Xunta de Galicia (research projects ED431C 2018/34 and Centro Singular de Investigacion de Galicia, accreditation 2016-2019) and by the European Union (European Regional Development Fund). Brais Cancela acknowledges the support of the Xunta de Galicia under its postdoctoral program.Xunta de Galicia; ED431C 2018/3

A scalable saliency-based Feature selection method with instance level information

Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge extraction. This allows the creation of compact models that are easier to interpret. Most of these techniques work over the whole dataset, but they are unable to provide the user with successful information when only instance information is needed. In short, given any example, classic feature selection algorithms do not give any information about which the most relevant information is, regarding this sample. This work aims to overcome this handicap by developing a novel feature selection method, called Saliency-based Feature Selection (SFS), based in deep-learning saliency techniques. Our experimental results will prove that this algorithm can be successfully used not only in Neural Networks, but also under any given architecture trained by using Gradient Descent techniques

On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility

[Abstract]: In classical survival analysis, it is assumed that all the individuals will experience the event of interest. However, if there is a proportion of subjects who will never experience the event, then a standard survival approach is not appropriate, and cure models should be considered instead. This paper deals with the problem of adapting a machine learning approach for classical survival analysis to a situation when cure (i.e., not suffering the event) is a possibility. Specifically, a brief review of cure models and recent machine learning methodologies is presented, and an adaptation of machine learning approaches to account for cured individuals is introduced. In order to validate the proposed methods, we present an extensive simulation study in which we compare the performance of the adapted machine learning algorithms with existing cure models. The results show the good behavior of the semiparametric or the nonparametric approaches, depending on the simulated scenario. The practical utility of the methodology is showcased through two real-world dataset illustrations. In the first one, the results show the gain of using the nonparametric mixture cure model approach. In the second example, the results show the poor performance of some machine learning methods for small sample sizes.This project was funded by the Xunta de Galicia (Axencia Galega de Innovación) Research projects COVID-19 presented in ISCIII IN845D 2020/26, Operational Program FEDER Galicia 2014–2020; by the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union European Regional Development Fund (ERDF)-Galicia 2014–2020 Program, by grant ED431G 2019/01; and by the Spanish Ministerio de Economía y Competitividad (research projects PID2019-109238GB-C22 and PID2021-128045OA-I00). ALC was sponsored by the BEATRIZ GALINDO JUNIOR Spanish Grant from MICINN (Ministerio de Ciencia e Innovación) with code BGP18/00154. ALC was partially supported by the MICINN Grant PID2020-113578RB-I00 and partial support of Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C-2020-14Xunta de Galicia; IN845D 2020/2

A review of green artificial intelligence: Towards a more sustainable future

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: Green artificial intelligence (AI) is more environmentally friendly and inclusive than conventional AI, as it not only produces accurate results without increasing the computational cost but also ensures that any researcher with a laptop can perform high-quality research without the need for costly cloud servers. This paper discusses green AI as a pivotal approach to enhancing the environmental sustainability of AI systems. Described are AI solutions for eco-friendly practices in other fields (green-by AI), strategies for designing energy-efficient machine learning (ML) algorithms and models (green-in AI), and tools for accurately measuring and optimizing energy consumption. Also examined are the role of regulations in promoting green AI and future directions for sustainable ML. Underscored is the importance of aligning AI practices with environmental considerations, fostering a more eco-conscious and energy-efficient future for AI systems.This work was supported by CITIC, a Research Center accredited by Galician University System, which is funded by "Consellería de Cultura, Educación e Universidade from Xunta de Galicia", supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by "Secretaría Xeral de Universidades" (Grant ED431G 2019/01). It was also partially funded by Xunta de Galicia/FEDER-UE under Grant ED431C 2022/44; Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 and "NextGenerationEU"/PRTR under Grants [PID2019-109238GB-C22; PID2021-128045OA-I00; ED2021-130599A-I00] and Ministry for Digital Transformation and Civil Service under grant TSI-100925-2023-1. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2022/4

Sustainable Transparency in Recommender Systems: Bayesian Ranking of Images for Explainability

Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for recommendations. Among the existing approaches for generating personalized explanations, using visual content created by the users is one particularly promising option, showing a potential to maximize transparency and user trust. Existing models for explaining recommendations in this context face limitations: sustainability has been a critical concern, as they often require substantial computational resources, leading to significant carbon emissions comparable to the Recommender Systems where they would be integrated. Moreover, most models employ surrogate learning goals that do not align with the objective of ranking the most effective personalized explanations for a given recommendation, leading to a suboptimal learning process and larger model sizes. To address these limitations, we present BRIE, a novel model designed to tackle the existing challenges by adopting a more adequate learning goal based on Bayesian Pairwise Ranking, enabling it to achieve consistently superior performance than state-of-the-art models in six real-world datasets, while exhibiting remarkable efficiency, emitting up to 75% less CO${_2}$ during training and inference with a model up to 64 times smaller than previous approaches

Machine learning techniques to predict different levels of hospital care of CoVid-19

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital care assistance (regular hospital admission or intensive care unit admission), during the course of their illness, using only demographic and clinical data. For this research, a data set of 10,454 patients from 14 hospitals in Galicia (Spain) was used. Each patient is characterized by 833 variables, two of which are age and gender and the other are records of diseases or conditions in their medical history. In addition, for each patient, his/her history of hospital or intensive care unit (ICU) admissions due to CoVid-19 is available. This clinical history will serve to label each patient and thus being able to assess the predictions of the model. Our aim is to identify which model delivers the best accuracies for both hospital and ICU admissions only using demographic variables and some structured clinical data, as well as identifying which of those are more relevant in both cases. The results obtained in the experimental study show that the best models are those based on oversampling as a preprocessing phase to balance the distribution of classes. Using these models and all the available features, we achieved an area under the curve (AUC) of 76.1% and 80.4% for predicting the need of hospital and ICU admissions, respectively. Furthermore, feature selection and oversampling techniques were applied and it has been experimentally verified that the relevant variables for the classification are age and gender, since only using these two features the performance of the models is not degraded for the two mentioned prediction problems.This research has been supported by GAIN (Galician Innovation Agency) and the Regional Ministry of Economy, Employment and Industry, Xunta de Galicia grant COV20/00604 through the ERDF Funds. Also, it has been possible thanks to the support of the Xunta de Galicia (Dirección Xeral de Saúde Pública) by providing the anonymous patient data. Also, it has been supported by the Xunta de Galicia (Grant ED431C 2018/34 and IN845D 2020/26 of the Axencia Galega de Innovación) with European Union ERDF funds. CITIC, as Research Center accredited by Galician University System, is funded by Consellería de Cultura, Educación e Universidades from Xunta de Galicia, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by Secretaría Xeral de Universidades (Grant ED431G 2019/01). Finally, we would also like to thank Prof. Ricardo Cao, as Chairman of the Committee of Experts for Mathematical Action against Coronavirus, for his kind request to collaborate in this projectXunta de Galicia; COV20/00604Xunta de Galicia; ED431C 2018/34Xunta de Galicia; IN845D 2020/26Xunta de Galicia; ED431G 2019/0

E2E-FS: An End-to-End Feature Selection Method for Neural Networks

[Abstract]: Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and LASSO variants. Both approaches are focused in different aspects: while the tree-based algorithms provide a clear explanation about which variables are being used to trigger a certain output, LASSO-like approaches sacrifice a detailed explanation in favor of increasing its accuracy. In this paper, we present a novel embedded feature selection algorithm, called End-to-End Feature Selection (E2E-FS), that aims to provide both accuracy and explainability in a clever way. Despite having non-convex regularization terms, our algorithm, similar to the LASSO approach, is solved with gradient descent techniques, introducing some restrictions that force the model to specifically select a maximum number of features that are going to be used subsequently by the classifier. Although these are hard restrictions, the experimental results obtained show that this algorithm can be used with any learning model that is trained using a gradient descent algorithm.We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research

Prior flexibility and performance in Tuy-Santiago military test

Introducción: La flexibilidad que tengan las personas, previamente a la realización de una actividad física-deportiva puede influir en los resultados de ésta. Por ello, nos propusimos analizar la influencia del nivel de flexibilidad previo sobre el rendimiento de los militares participantes en la prueba de largo recorrido Tuy-Santiago, edición 2014. Material y Método: Estudio transversal en el que han participado 135 militares voluntarios, con una media de edad de 28,47±4,54 años. Esta prueba deportivo-militar consta de un recorrido aproximado de 120 km, con una serie de pruebas militares realizadas a lo largo del mismo. La flexibilidad fue valorada por medio de la prueba Sit and Reach. Se analizaron las variables usando estadísticos descriptivos y tablas de contingencia, para estudiar las diferencias existentes entre las variables, con el programa estadístico SPSS. Resultados: Existen diferencias significativas entre los valores medios de la de la prueba de flexibilidad entre patrullas antes de comenzar la prueba, además de correlaciones significativas con determinados tramos o pruebas que integraban el recorrido completo. Conclusiones: Parece haber una correlación significativa entre la flexibilidad previa y el rendimiento alcanzado para la muestra analizada en la prueba Tuy-Santiago.Introduction: The flexibility degree of different athletes before any sport event is being suggested to influence the performance in it. Because of that, the objective of this study was to analyze the influence of the previous flexibility degree of the servicemen participating in the military sport event Tuy-Santiago in 2014. Material and Method: Cross sectional study involving 135 voluntary servicemen, with an average age of 28,47±4,54 years. This military sport event has an approximate distance of 120Km, and different military test are included in it. Flexibility has been measured by the Sit and Reach test. Variables have been analyzed using descriptive statistics and crosstabs using the SPSS software. Results: There are significant differences between mean values for flexibility among the patrols. Additionally, significant correlation was observed between flexibility and different parts or test of the event. Conclusions: There seems to be a significant correlation between previous flexibility and the performance achieved in the military sport event Tuy-Santiago for the analyzed sample