Trailgazers: A Scoping Study of Footfall Sensors to Aid Tourist Trail Management in Ireland and Other Atlantic Areas of Europe

This paper examines the current state of the art of commercially available outdoor footfall sensor technologies and defines individually tailored solutions for the walking trails involved in an ongoing research project. Effective implementation of footfall sensors can facilitate quantitative analysis of user patterns, inform maintenance schedules and assist in achieving management objectives, such as identifying future user trends like cyclo-tourism. This paper is informed by primary research conducted for the EU funded project TrailGazersBid (hereafter referred to as TrailGazers), led by Donegal County Council, and has Sligo County Council and Causeway Coast and Glens Council (NI) among the 10 project partners. The project involves three trails in Ireland and five other trails from Europe for comparison. It incorporates the footfall capture and management experiences of trail management within the EU Atlantic area and desk-based research on current footfall technologies and data capture strategies. We have examined 6 individual types of sensor and discuss the advantages and disadvantages of each. We provide key learnings and insights that can help to inform trail managers on sensor options, along with a decision-making tool based on the key factors of the power source and mounting method. The research findings can also be applied to other outdoor footfall monitoring scenarios

Detección y seguimiento de una persona en una habitación

[ES] Existen diferentes métodos de detección de objetos utilizando diferentes dispositivos como cámaras web, Kinect, cámaras estereoscópicas, etc. La mayoría de estos estudios están enfocados a la detección de objetos con fines de seguridad en la sociedad, ya sea detectar robos, controlar el tráfico en las carreteras, contar personas en una escena, etc. Pero no existen sistemas para detectar alteraciones en el movimiento de las personas, o juegos para niños, pero con fines educativos. El presente trabajo final de Máster está motivado por la necesidad de seguimiento de una persona en una habitación para el proyecto CHILDMNEMOS. A lo largo de esta tesina se han desarrollado cuatro algoritmos con el fin de conseguir detectar a una persona en movimiento con dispositivos ubicados en el techo de una escena, de tal forma que detecte a la persona verticalmente y no de frente. Para ello se realizó un análisis de posibles algoritmos que podrían responder a nuestra necesidad utilizando dos dispositivos diferentes de captura de fotogramas de video; utilizamos una cámara Logitech 9000 y Kinect. Además de usar librerías para visión artificial y para el desarrollo de aplicaciones para Kinect. Estas librerías son OpenCV y OpenNI, respectivamente. Como pasos en los algoritmos desarrollados, se encuentran técnicas conocidas, como Sustracción de fondo, Mixture of Gauss, Filtro de Kalman; y también funciones de la librería OpenNI, que funcionan con Kinect. Se compararán los algoritmos y los dispositivos, para conocer qué algoritmo y qué dispositivo son los que ofrecen mejores resultados. Al realizar los experimentos, se obtuvieron mejores resultados con un algoritmo desarrollado para Kinect, usando la técnica que se planteó, que consiste en analizar solo una región de la escena capturada por Kinect (Cabeza y Hombros de la persona) el cual permitió sustraer el fondo; además de usar filtros para eliminar el ruido y sombra; y para el seguimiento se usó el Filtro de Kalman.[EN] There are different methods for detecting objects using different devices such as web cameras, Kinect, stereoscopic cameras, etc. Most of these studies are focused on detection of objects for security in society, whether detect theft, monitor traffic on the roads, counting people in a scene, etc. But there aren¿t systems to detect changes in the movement of people, or children's games, but for educational purposes. This Thesis is motivated by the need to track a person in a room for the project CHILDMNEMOS. Throughout this thesis four algorithms have been developed in order to detect a person get moving with devices located in the ceiling of a scene, so the algorithms and the devices detects the person vertically and not in front of the person. This was achieved by an analysis of possible algorithms that could answer our need using two different devices capture video frames, we use a Logitech 9000 and Kinect camera. Besides using artificial vision and libraries for developing applications for Kinect. These libraries are OpenCV and OpenNI, respectively. As steps in the algorithms developed are known techniques such as background subtraction, Mixture of Gauss, Kalman Filter, and OpenNI library functions that work with Kinect. Will compare the algorithms and devices for which algorithm and which device are those that offer better results. In conducting the experiments, better results were obtained with an algorithm developed for Kinect, using the technique proposed, which consists of analyzing only a region of the scene captured by Kinect (Head and shoulders of the person) which allowed background subtraction in addition to using filters to remove noise and shadow, and for Tracking Kalman filter was used.Olguin Rivas, L. (2013). Detección y seguimiento de una persona en una habitación. http://hdl.handle.net/10251/38278Archivo delegad

A low power people counting system based on a visoin sensor working on contrast

The presented demonstrator implements a person counting system using a single overhead sensor and counting the number of people going in and out of an observed area. It is based on a low-power vision sensor, which extracts the contrast of the image in a binary form, implements motion by differencing two successive frames and dispatches the asserted pixels through an address-driven data representation. A lightweight counting people algorithm has been developed, which is based on virtual loop. The demonstrator consists of the vision sensor interfaced with the FPGA and linked to a PC with a graphical interface

People Detection and Tracking Based on Stereovision and Kalman Filter

[ES] Los sistemas de conteo de personas son extensamente utilizados en aplicaciones de vigilancia. En este artículo se presenta una aplicación para realizar conteo de personas a través de un sistema de estereovisión. Este sistema obtiene tasas de conteo de las personas en movimiento que atraviesan la zona de conteo recogida por el sistema estéreo distinguiendo entrada y salida. Para realizar este conteo se precisan dos fases fundamentales: detección y seguimiento. La detección se basa en la búsqueda de las cabezas de las personas por medio de una correlación de la imagen preprocesada con distintos patrones circulares, filtrando dichas detecciones por estereovisión en función de la altura. El seguimiento se lleva a cabo mediante una algoritmo de múltiples hipótesis basado en filtro de Kalman. Por último, se realiza el conteo según el camino seguido por las trayectorias. Se ha experimentado con un conjunto de vídeos reales tomados en distintas zonas de tránsito en interiores de edificios, alcanzando tasas que oscilan entre un 87% y un 98% de acierto según la cantidad de flujo de personas que atraviesan la zona de conteo de forma simultánea. En los distintos vídeos utilizados como prueba se han reproducido todo tipo de situaciones adversas, como oclusiones, personas en grupo en diferentes sentidos, cambios de iluminación, etc.[EN] The people counting systems are widely used in surveillance applications. This article presents an application for counting people through a stereovision system. This system obtains counting rates of people moving through the counting area, distinguishing between input and output. To achieve this aim is required two basic steps: detection and tracking. The detection step is based on correlation through a pre-processed image with various circular patterns in order to search people's heads, filtering these detections by stereovision depending on the height. The people tracking is carried out through a multiple hypothesis algorithm based on the Kalman filter. Finally, people counting is done according to the trajectory followed by the person. To validate the algorithm have been used several real videos taken from different transit areas inside buildings, reaching rates ranging between 87% and 98% accuracy depending on the number of people crossing the counting zone simultaneously. In these videos occur several adverse situations, such as occlusions, people in groups in different directions, lighting changes, etc.Este trabajo ha sido realizado gracias al Programa Nacional de Diseño y Producción Industrial del Ministerio de Ciencia y Tecnología, a través del proyecto ESPIRA (ref. DPI2009-10143) y a la Universidad de Alcalá (ref.UAH2011/EXP-001), a través del proyecto ”Sistema de Arrays de Cámaras Inteligentes (SACI)”.García, J.; Gardel, A.; Bravo, I.; Lázaro, JL.; Martínez, M.; Rodríguez, D. (2012). Detección y Seguimiento de Personas Basado en Estereovisión y Filtro de Kalman. Revista Iberoamericana de Automática e Informática industrial. 9(4):453-461. https://doi.org/10.1016/j.riai.2012.09.012OJS45346194Donate, A., Liu, X., & Collins, E. G. (2011). Efficient Path-Based Stereo Matching With Subpixel Accuracy. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(1), 183-195. doi:10.1109/tsmcb.2010.2049839Englebienne, G., van Oosterhout, T., Krose, B., 2009. Tracking in sparse multi- camera setups using stereo vision. In: Proc. Third ACM/IEEE Int. Conf. Distributed Smart Cameras ICDSC 2009. pp. 1-6.Mucientes, M., Burgard, W., oct. (2006). Multiple hypothesis tracking of clusters of people. In: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on. pp. 692-697.Rizzon, L., Massari, N., Gottardi, M., Gasparini, L., 2009. A low-power people counting system based on a vision sensor working on contrast. In: Proc. IEEE Int. Symp. Circuits and Systems ISCAS 2009.Xu, H., Lv, P., Meng, L., 2010. A people counting system based on head- shoulder detection and tracking in surveillance video. In: Proc. Int Computer Design and Applications (ICCDA) Conf. Vol. 1