Motion prediction and interaction localisation of people in crowds

PhDThe ability to analyse and predict the movement of people in crowded scenarios can be of fundamental importance for tracking across multiple cameras and interaction localisation. In this thesis, we propose a person re-identification method that takes into account the spatial location of cameras using a plan of the locale and the potential paths people can follow in the unobserved areas. These potential paths are generated using two models. In the first, people’s trajectories are constrained to pass through a set of areas of interest (landmarks) in the site. In the second we integrate a goal-driven approach to the Social Force Model (SFM), initially introduced for crowd simulation. SFM models the desire of people to reach specific interest points (goals) in a site, such as exits, shops, seats and meeting points while avoiding walls and barriers. Trajectory propagation creates the possible re-identification candidates, on which association of people across cameras is performed using spatial location of the candidates and appearance features extracted around a person’s head. We validate the proposed method in a challenging scenario from London Gatwick airport and compare it to state-of-the-art person re-identification methods. Moreover, we perform detection and tracking of interacting people in a framework based on SFM that analyses people’s trajectories. The method embeds plausible human behaviours to predict interactions in a crowd by iteratively minimising the error between predictions and measurements. We model people approaching a group and restrict the group formation based on the relative velocity of candidate group members. The detected groups are then tracked by linking their centres of interaction over time using a buffered graph-based tracker. We show how the proposed framework outperforms existing group localisation techniques on three publicly available datasets

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Evaluación de descriptores para la detección automática de fallas en fabricación utilizando máquinas de soporte vectorial

Este documento presenta la evaluación de un método de clasificación de fallas en productos terminados utilizando la combinación de descriptores de color, forma y textura. Se utiliza una Máquina de Vectores de Soporte multiclase (SVM-Support Vector Machine) y se construye una base de datos anotada capturando botellas de plástico con 11 situaciones de fabricación entre botellas en buen estado y botellas con imperfectos como rasgaduras, golpes, hendiduras, etc; bajo diferentes condiciones no controladas (ruido, iluminación, escala, entre otras). La etapa de fusión propone una combinación lineal de características y para calcular el desempeño de descriptores y fusión de datos, se utilizó una metodología de validación cruzada aplicando el método de Montecarlo. La configuración de SVM utiliza la metodología multiclase “One-vs-All” con Kernel Radial Gaussiano. La detección se realiza inicialmente aplicando descriptores individuales y combinados

Metodología para la detección de telas en la industria textil utilizando cámaras

Con el alto crecimiento de la industria textil, y con su gran participación en el comercio y la economía del país y del mundo, se exige que con el mejoramiento de las tecnologías y de la ciencia, se avance también en la tecnología que manejan las telas, por esto en cuanto a nuevas telas se implementan fibras que cada vez ofrecen mayores beneficios en diferentes campos, como son las telas que se usan para la ropa deportiva, o para crear trajes de protección industrial, entre muchas otras cosas; también con el cambio climático y la preocupación que esto trae alrededor del mundo, los esfuerzos en la creación de telas que puedan proteger a las personas de los rayos UV del sol, o del frio crecen, ofreciendo cada día mejoras en las fibras y calidades de las telas. Por esto el proceso de clasificación de telas es una tarea que no se puede dejar al azar, y aprovechando tecnologías como las técnicas de visión por computador, en donde maquinas realizan estos procesos de manera eficiente, y representan también un bajo costo para la industria textil, el uso de estas herramientas va en aumento; por ende la demanda de desarrollo de algoritmos eficientes crece cada día, ya que estos todavía presentan falencias, el estudio no termina, y con el avance de los conocimientos en los diferentes campos que interfieren en el proceso de toma de datos y clasificación, como lo es el desarrollo electrónico que no para, y las nuevas tecnologías en cámaras, las mejoras crecen en igual proporción

Action intention recognition for proactive human assistance in domestic environments

The current Master’s Thesis in Automatics, Control and Robotics covers the development and implementation of an Action Intention Recognition algorithm for proactive human assistance in domestic environments. The proposed solution is based on the use of data provided by a real time RGBD Object Recognition process which captures object state changes inside a defined region of interest of the domestic environment setup. A background analysis is performed to analyze state of the art approaches to both real time RGBD object recognition and action intention recognition methods. The preliminary analysis serves as the base for the proposal of a new volume descriptor for object categorization and an improved formalism for Activation Spreading Networks in the context of action intention recognition. Several tests are performed to study the performance of the proposed solution and its results are analyzed to define the conclusions of the project and propose future work. Finally, the project budget and environmental impact as well as the project schedule are presented and briefly discusse

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

[EN] This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as "objects" to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein.Suarez-Paez, J.; Salcedo-Gonzalez, M.; Climente, A.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE.; Pérez Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information. 10(12):1-19. https://doi.org/10.3390/info10120365S1191012Wang, L., Rodriguez, R. M., & Wang, Y.-M. 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