Solostocks: commercial business model assessment & Fit with customer needs

CEMSThis Business Project aimed at providing a better understanding of SoloStocks’ current customer as a driver for better monetization, growth and a sustainable long term positioning of the company in the market. Lack of professionalization of sellers and of a tailored value proposition were discovered to be the two main pain points of clients that were preventing satisfaction with the service provided. Moreover, different personas were identified using the platform in need for additional or distinctive features. Best practices of the market served as base for innovative recommendations on how to provide more value to the customers

Automatic human action recognition from video using Hidden Markov model

Posture classification is a key process for evaluating the behaviors of human being. Computer vision techniques can play a vital role in automating the overall process, however, occlusions, cluttered environment and illumination changes can make the whole task difficult. Using multiple cameras and warping known object appearance into the occluded view can solve the occlusion problem. In this paper, we present an automatic human detection and action recognition system using Hidden Markov Model and bag of Words. Background subtraction is performed using Gaussian mixture model. The algorithm is able to perform robust detection in the cluttered environment and severe occlusions. The novelty of this work is the dataset used. A private dataset has been created for this research at university of Minho. The experimental results show promising results

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012

Does renewable energies’ usage act as a shield against oil price changes?

The purpose of this dissertation is to contribute to the existing literature on equity markets and energy prices by studying the impact of oil price changes on several American and European companies, taking into consideration their level of renewable energies’ usage within the total energy consumption. The results show that in fact there is an overall negative impact of oil price changes. However, when we split the oil price changes in positive and negative ones, it seems that their impact is symmetric, being positive (negative) when the change is negative (positive). Using the level of renewable energies in the estimation models, the conclusion that arrives is that there could be an optimal maximum level of green energies’ usage, for which the companies can somewhat benefit from a protection against oil price changes, thus hedging against its risk

Real-time intelligent decision support system for bridges structures behavior prediction

There is an increasing need of deploying automatic real-time decision support systems for civil engineering structures, making use of prediction models based in Artificial Intelligence techniques (e.g., Artificial Neural Networks) to support the monitoring and prediction activities. Past experiments with Data Mining (DM) techniques and tools opened room for the development of such a real-time Decision Support System. However, it is necessary to test this approach in a real environment, using real-time sensors monitoring. This study presents the development of prediction models for structures behavior and a novel architecture for operating in a real-time system

Moving object detection unaffected by cast shadows, highlights and ghosts

IEEE Copyright Policies: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.This paper describes a new approach to perform segmentation of moving objects in real-time from images acquired by a fixed color video camera and is the first tool of a major project that aspires to recognize abnormal human behavior in public areas. The moving objects detection is based on background subtraction and it is unaffected by changes in illumination, i.e., cast shadows and highlights. Furthermore it does not require a special attention during the initialization process, due to its ability to detect and rectify ghosts. The results show that with image resolutions of 380x280 at 24 bits per pixel, the time spent in the segmentation process is around 80ms, in a 32 bits 3GHz processor based computer.Fundação para a Ciência e a Tecnologia (FCT

Previsão de eventos anormais em sistemas de vídeo-vigilância

Este trabalho tem como propósito a detecção e previsão de comportamentos passíveis de originar uma quebra de segurança. Tais comportamentos são reconhecidos por meio da observação de padrões de actividade humana, extraídos de sequências de imagens digitalizadas adquiridas por intermédio de uma câmara de vídeo a cores, monocular e fixa. A aferição dos comportamentos é suportada pela informação resultante dos processos de detecção, classificação e seguimento de objectos em movimento, minimizando a utilização de informação de contexto na cena observada, e sem recurso a descrições de comportamentos previamente definidos. Para a detecção e previsão automática de comportamentos desenvolveu-se um novo classificador (Dynamic Oriented Graph) proposto no âmbito deste trabalho e que, utilizando os dados provenientes das funções de processamento e análise de imagem, permite modelar sequências temporais. O sistema, constituído pela junção das várias componentes desenvolvidas e implementado numa câmara de vídeo inteligente, foi testado com um conjunto de dados sintéticos

Prediction of abnormal behaviors for intelligent video surveillance systems

IEEE Copyright Policies This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.The OBSERVER is a video surveillance system that detects and predicts abnormal behaviors aiming at the intelligent surveillance concept. The system acquires color images from a stationary video camera and applies state of the art algorithms to segment, track and classify moving objects. In this paper we present the behavior analysis module of the system. A novel method, called Dynamic Oriented Graph (DOG) is used to detect and predict abnormal behaviors, using real-time unsupervised learning. The DOG method characterizes observed actions by means of a structure of unidirectional connected nodes, each one defining a region in the hyperspace of attributes measured from the observed moving objects and having assigned a probability to generate an abnormal behavior. An experimental evaluation with synthetic data was held, where the DOG method outperforms the previously used N-ary Trees classifier.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/17259/2004

N-ary trees classifier

This paper addresses the problem of automatic detection and prediction of abnormal human behaviours in public spaces. For this propose a novel classifier, called N-ary trees, is presented. The classifier processes time series of attributes like the object position, velocity, perimeter and area, to infer the type of action performed. This innovative classifier can detect three types of events: normal; unusual; or abnormal events. In order to evaluate the performance of the N-ary trees classifier, we carry out a preliminary study with 180 synthetic tracks and one restricted area. The results revealed a great level of accuracy and that the proposed method can be used in surveillance systems.Fundação para a Ciência e a Tecnologia (FCT)

The OBSERVER: an intelligent and automated video surveillance system

Comunicação apresentada na ICIAR, 3, Póvoa de Varzim, Portugal, 2006.In this work we present a new approach to learn, detect and predict unusual and abnormal behaviors of people, groups and vehicles in real-time. The proposed OBSERVER video surveillance system acquires images from a stationary color video camera and applies state-of-the-art algorithms to segment and track moving objects. The segmentation is based in a background subtraction algorithm with cast shadows, highlights and ghost’s detection and removal. To robustly track objects in the scene, a technique based on appearance models was used. The OBSERVER is capable of identifying three types of behaviors (normal, unusual and abnormal actions). This achievement was possible due to the novel N-ary tree classifier, which was successfully tested on synthetic data.Fundação para a Ciência e a Tecnologia (FCT)