Cognitive and Computational Neuroscience: Principles, Algorithms, and Applications in Surveillance Context

Today, working with human behavior is vitally important, especially if we consider the impact neuroscience and security systems. The responsibility of monitoring in a conventional way is in charge of a human agent (vigilant). On the other hand, a vigilant cannot be aware at all times. He can only be aware for 20 minutes which is the time he can monitor four cameras simultaneously; after that, the task of surveillance ceases to make sense. This reveals one of the shortcomings of surveillance (SV) systems. Whether a surveillance system provides a warning of an activity or situation makes it as important as the selection of the technological elements that allowed it to be captured. Security systems based on intelligent technologies have had an accelerated development in recent times detection and identification of car registration numbers, detection of static objects in tracks, and detection of pedestrians circulating on not permitted routes. The reuse of methodologies, procedures, and ontologies is described in this chapter of the book

A review on intelligent monitoring and activity interpretation

This survey paper provides a tour of the various monitoring and activity interpretation frameworks found in the literature. The needs of monitoring and interpretation systems are presented in relation to the area where they have been developed or applied. Their evolution is studied to better understand the characteristics of current systems. After this, the main features of monitoring and activity interpretation systems are defined.Este trabajo presenta una revisión de los marcos de trabajo para monitorización e interpretación de actividades presentes en la literatura. Dependiendo del área donde dichos marcos se han desarrollado o aplicado, se han identificado diferentes necesidades. Además, para comprender mejor las particularidades de los marcos de trabajo, esta revisión realiza un recorrido por su evolución histórica. Posteriormente, se definirían las principales características de los sistemas de monitorización e interpretación de actividades.This work was partially supported by Spanish Ministerio de Economía y Competitividad / FEDER under DPI2016-80894-R grant

A practical approach for active camera coordination based on a fusion-driven multi-agent system

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02 and CAM CONTEXTS (S2009/TIC-1485).Publicad

A multi-modal approach for activity classification and fall detection

"Special issue : Intelligent multisensory systems in support of information society"The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.This work is funded by National Funds through the FCT-Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst- OE/EEI/UI0752/2011. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009). This work is also partially supported by the Spanish Ministerio de Economía y Competitividad / FEDER under project TIN2010-20845-C03-01

Software architecture for smart emotion recognition and regulation of the ageing adult

This paper introduces the architecture of an emotion-aware ambient intelligent and gerontechnological project named “Improvement of the Elderly Quality of Life and Care through Smart Emotion Regulation”. The objective of the proposal is to find solutions for improving the quality of life and care of the elderly who can or want to continue living at home by using emotion regulation techniques. A series of sensors is used for monitoring the elderlies’ facial and gestural expression, activity and behaviour, as well as relevant physiological data. This way the older people’s emotions are inferred and recognized. Music, colour and light are the stimulating means to regulate their emotions towards a positive and pleasant mood. Then, the paper proposes a gerontechnological software architecture that enables real-time, continuous monitoring of the elderly and provides the best-tailored reactions of the ambience in order to regulate the older person’s emotions towards a positive mood. After describing the benefits of the approach for emotion recognition and regulation in the elderly, the eight levels that compose the architecture are described.This work was partially supported by Spanish Ministerio de Economía y Competitividad/FEDER under TIN2013-47074-C2-1-R grant. José Carlos Castillo was partially supported by a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism, operated by Universidad Complutense de Madrid.Publicad

Technologies for safe and resilient earthmoving operations: A systematic literature review

Resilience engineering relates to the ability of a system to anticipate, prepare, and respond to predicted and unpredicted disruptions. It necessitates the use of monitoring and object detection technologies to ensure system safety in excavation systems. Given the increased investment and speed of improvement in technologies, it is necessary to review the types of technology available and how they contribute to excavation system safety. A systematic literature review was conducted which identified and classified the existing monitoring and object detection technologies, and introduced essential enablers for reliable and effective monitoring and object detection systems including: 1) the application of multisensory and data fusion approaches, and 2) system-level application of technologies. This study also identified the developed functionalities for accident anticipation, prevention and response to safety hazards during excavation, as well as those that facilitate learning in the system. The existing research gaps and future direction of research have been discussed

Developing a person guidance module for hospital robots

This dissertation describes the design and implementation of the Person Guidance Module (PGM) that enables the IWARD (Intelligent Robot Swarm for attendance, Recognition, Cleaning and delivery) base robot to offer route guidance service to the patients or visitors inside the hospital arena. One of the common problems encountered in huge hospital buildings today is foreigners not being able to find their way around in the hospital. Although there are a variety of guide robots currently existing on the market and offering a wide range of guidance and related activities, they do not fit into the modular concept of the IWARD project. The PGM features a robust and foolproof non-hierarchical sensor fusion approach of an active RFID, stereovision and cricket mote sensor for guiding a patient to the X-ray room, or a visitor to a patient’s ward in every possible scenario in a complex, dynamic and crowded hospital environment. Moreover, the speed of the robot can be adjusted automatically according to the pace of the follower for physical comfort using this system. Furthermore, the module performs these tasks in any unconstructed environment solely from a robot’s onboard perceptual resources in order to limit the hardware installation costs and therefore the indoor setting support. Similar comprehensive solution in one single platform has remained elusive in existing literature. The finished module can be connected to any IWARD base robot using quick-change mechanical connections and standard electrical connections. The PGM module box is equipped with a Gumstix embedded computer for all module computing which is powered up automatically once the module box is inserted into the robot. In line with the general software architecture of the IWARD project, all software modules are developed as Orca2 components and cross-complied for Gumstix’s XScale processor. To support standardized communication between different software components, Internet Communications Engine (Ice) has been used as middleware. Additionally, plug-and-play capabilities have been developed and incorporated so that swarm system is aware at all times of which robot is equipped with PGM. Finally, in several field trials in hospital environments, the person guidance module has shown its suitability for a challenging real-world application as well as the necessary user acceptance

Spatio-temporal action localization with Deep Learning

Dissertação de mestrado em Engenharia InformáticaThe system that detects and identifies human activities are named human action recognition. On the video approach, human activity is classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action, namely gestures, actions, interactions, and activities, which is challenging for video Human action recognition to capture valuable and discriminative features because of the human body’s variations. So, deep learning techniques have provided practical applications in multiple fields of signal processing, usually surpassing traditional signal processing on a large scale. Recently, several applications, namely surveillance, human-computer interaction, and video recovery based on its content, have studied violence’s detection and recognition. In recent years there has been a rapid growth in the production and consumption of a wide variety of video data due to the popularization of high quality and relatively low-price video devices. Smartphones and digital cameras contributed a lot to this factor. At the same time, there are about 300 hours of video data updates every minute on YouTube. Along with the growing production of video data, new technologies such as video captioning, answering video surveys, and video-based activity/event detection are emerging every day. From the video input data, the detection of human activity indicates which activity is contained in the video and locates the regions in the video where the activity occurs. This dissertation has conducted an experiment to identify and detect violence with spatial action localization, adapting a public dataset for effect. The idea was used an annotated dataset of general action recognition and adapted only for violence detection.O sistema que deteta e identifica as atividades humanas é denominado reconhecimento da ação humana. Na abordagem por vídeo, a atividade humana é classificada em quatro categorias diferentes, dependendo da complexidade das etapas e do número de partes do corpo envolvidas na ação, a saber, gestos, ações, interações e atividades, o que é desafiador para o reconhecimento da ação humana do vídeo para capturar características valiosas e discriminativas devido às variações do corpo humano. Portanto, as técnicas de deep learning forneceram aplicações práticas em vários campos de processamento de sinal, geralmente superando o processamento de sinal tradicional em grande escala. Recentemente, várias aplicações, nomeadamente na vigilância, interação humano computador e recuperação de vídeo com base no seu conteúdo, estudaram a deteção e o reconhecimento da violência. Nos últimos anos, tem havido um rápido crescimento na produção e consumo de uma ampla variedade de dados de vídeo devido à popularização de dispositivos de vídeo de alta qualidade e preços relativamente baixos. Smartphones e cameras digitais contribuíram muito para esse fator. Ao mesmo tempo, há cerca de 300 horas de atualizações de dados de vídeo a cada minuto no YouTube. Junto com a produção crescente de dados de vídeo, novas tecnologias, como legendagem de vídeo, respostas a pesquisas de vídeo e deteção de eventos / atividades baseadas em vídeo estão surgindo todos os dias. A partir dos dados de entrada de vídeo, a deteção de atividade humana indica qual atividade está contida no vídeo e localiza as regiões no vídeo onde a atividade ocorre. Esta dissertação conduziu uma experiência para identificar e detetar violência com localização espacial, adaptando um dataset público para efeito. A ideia foi usada um conjunto de dados anotado de reconhecimento de ações gerais e adaptá-la apenas para deteção de violência