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

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

Empirical Investigation of Influencing Factors Regarding Offshore Outsourcing Decision of Application Maintenance

Global Software Development (GSD) has been an emerging trend in the development of software globally, for the last two decades. Information Technology (IT) outsourcing includes application development, application maintenance, infrastructure management and business process outsourcing. Software maintenance aims to keep the IT system operational and to fulfill the client requirements. The maintenance is considered the longest phase of software life cycle that consumes about 60-70% of the total software budget. Maintenance of software is not only time consuming but also requires a significant human resources' ratio. Mostly, software acquisition and maintenance consume a big portion of the total IT budget. The current study aims to evaluate the findings of the systematic literature review and to derive a list of critical success factors regarding offshore outsourcing decision of application maintenance. Thus, an empirical study is performed to validate the influencing factors that were identified by using systematic literature review. These factors are further validated by 93 outsourcing experts from 30 different countries. The collected data through online survey is analyzed based on variables such as respondents experience level, respondents' locations (continents), experts' positions. Similarly, the data is analysed based on Chi square test (linear by linear association) and Spearman Rank Correlation. Additionally, the identified factors through survey and systematic literature review are ranked by two different methods. Consequently, a project assessment model is proposed, based on the critical success factors for the sourcing decision of application maintenance.Qatar University [QUHI-CBE-21/22-1]

Analyzing factors that influence offshore outsourcing decision of application maintenance

Application maintenance consumes a considerable amount of an organization's time and resources each year. Almost 60% of IT budget is spent alone on application maintenance. The reason of offshore outsourcing of application maintenance is not only the reduction of maintenance cost but to free up the resources and to keep the focus on core products. Offshore outsourcing is a common business strategy that is used by companies to achieve cost savings about 20-50%. However, the decision making process of application maintenance is a complex phenomenon. It is based on a set of influencing factors, clients' requirements and nature of the project. Hence, the current study is aimed at the in-depth investigation of the complex sourcing decision process of application maintenance. Accordingly, a systematic literature review is performed to determine the influencing factors and critical success factors that will be used by the decision makers for the evaluation of projects before making the outsourcing decisions. A total of 15 influencing factors out of 52 selected papers were identified. Based on the defined criteria, amongst the identified factors, only 10 factors were ranked as critical success factors, which are employees' skills, cost, legal requirements, infrastructure, communication, knowledge transfer, maturity level, project management, language barrier and frequent requirements changes. Consequently, a sourcing model was proposed based on the identified critical success factors that help the IT managers and domain experts in making appropriate outsourcing decisions.Qatar University [IRCC-2020-009]

Making the Sourcing Decision of Software Maintenance and Information Technology

Outsourcing has been getting a significant growth for the last few years. Organizations tend to outsource Information Technology (IT), primarily to take advantage of the availability of qualified, trained and skilled workforce in low cost countries across the globe. Outsourcing of IT and software maintenance seem very promising, but a number of factors, risks, and challenges associated with the outsourcing process that make the sourcing decision very complicated. The present study aimed at gaining in-depth understanding of the three aspects of outsourcing, namely; perceived benefits of IT outsourcing, influencing factors of IT outsourcing and software maintenance offshoring. The findings of the current study will lead us to develop a sourcing framework for outsourcing decision as well as a decision support system for software maintenance. A systematic literature review is performed that presents perceived benefits of IT outsourcing, the influencing factors of IT outsourcing and software maintenance. Furthermore, the identified factors are analyzed based on their occurrences in literature as well as chi square test is performed to derive the significant differences amongst the factors based on decades. Similarly, critical success factors are derived both for IT outsourcing and software maintenance offshoring. Our article shows that how the critical success factors impact the IT as well the software maintenance in global delivery perspective. The findings of the current study will help the IT experts and decision makers in making suitable sourcing decisions.Qatar University [IRCC-2020-009]

An intelligent system for detection and identification of human behaviors from unconstrained video

The MAP-i Doctoral Programme in Computer Science, of the Universities of Minho, Aveiro and PortoIn this work, an intelligent system for human action recognition and destination trajectory prediction from unconstrained video is presented. For the automatic human action recognition, the video is processed frame by frame and blob analysis is performed to look for any active blobs. In order to select only humans and to remove noise, we defined a minimum pixel area for blob selection, which was set to 2000 pixels (e.g., 45x45, 60x34) after some preliminary experiments. For background subtraction, we tested the Gaussian Mixture Models (GMMs), for separating the foreground pixels from the background. This detector works on data collected from a stationary camera and compares a color or gray scale video frame to a background model to figure out whether it is part of the background or foreground. It then computes a foreground mask based on Gaussian Mixture Models (GMM). The human action recognition of our system is based on Hidden Markov Model (HMM) using the Bag of Words method (BoWs) (with boundary of humans as the main feature). Time-sequential images of human actions were transformed into feature vectors. We targeted two action classes: walking and sitting. Overall, high accuracy results were achieved. The proposed system for trajectory destination area prediction adopts a passive collection of video, works directly with raw video data and extracts motion features (position, velocity, and acceleration) from automatically detected human skeletons (with positions of the body of mass, head, hands and legs). It includes three main modules: human blob detection, an enhanced version of human blob detection to achieve improved silhouette; star skeleton detection, encompassing shadow removal and contour peak detection; and the final destination area prediction, based on preprocessing (dimensionality reduction and balancing sampling methods) and four classification methods: Multinomial logistic regression (MLR), Multilayer Perceptron (MLP) network, Random Forests (RF) and Support Vector Machine (SVM). For the second main task of this PhD, trajectory destination area prediction, the human blob detection was modified by adding another component: shadow and highlight removal. We also replaced the GMM background segmentation method by a simpler background subtraction method, such that the latter provided faster and better results. As a case study, we analyzed an exterior scene from a university campus that includes five main destination areas and 348 pedestrian trajectories from 171 videos. A realistic growing window evaluation was used in order to test four classifiers under six data processing combinations. The best results were achieved by the all inputs, undersampling and RF model. This model obtained the best global Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) analysis, which corresponds to a high quality class discrimination (median AUC of 87%). Moreover, the suggested model provided very good ahead time predictions for four of the classes (A, B, C and D) and a reasonable ahead discrimination performance for class E. While we achieved interesting results in the analyzed university campus case study, we believe the proposed system is useful for other application scenarios.Neste trabalho é proposto um sistema inteligente para reconhecimento de ações humanas e previsão da área final de trajetórias pedestres a partir de vídeos que capturam espaços reais de movimentação humana. O vídeo é processado via uma sequência de imagens, sendo a deteção de humanos obtida via uma identificação de um objeto móvel com uma área mínima de 2000 pixels (por exemplo definido via um retângulo de 45x45 ou 60x34). Para a eliminação do fundo (ambiente), foram utilizados Gaussian Mixture Models (GMM), sendo que o reconhecimento de ações baseou-se em modelos de Hidden Markov (HMM). O sistema desenvolvido foi testado para detetar duas ações, caminhar e sentar, tendo sido obtida uma elevada acuidade. Quanto à previsão dá área final de trajetórias humanas, foi utilizado uma coleta passiva de vídeo. Os dados em bruto foram processados de modo a extrair atributos de movimento (posição, velocidade e aceleração) de esqueletos compostos por 5 pontos (cabeça, mãos e pés) estimados automaticamente a partir de um contorno humano. O sistema desenvolvido incluí três módulos principais: deteção humana (inclui uma melhoria do processamento de imagem via: uma subtração de fundo mais simples e mais eficaz; e uma remoção de sombras e brilhos), deteção de esqueletos humanos e previsão da área final da trajetória pedestre. Este último módulo é composto por métodos de processamento de dados (via compressão de atributos e de balanceamento dos dados de treino) e algoritmos de classificação: regressão logística, redes neuronais, Random Forest (RF) e máquinas de vetores de suporte. Como caso de estudo, foi analisado um cenário real e exterior de um campus universitário e que inclui: cinco entradas e saídas principais (A, B, C, D e E), 348 trajetórias pedestres e 171 vídeos. Foi testada uma avaliação robusta via um método de treino incremental, que permitiu avaliar o desempenho dos quatro classificadores em seis configurações distintas de processamento dos dados. Os melhores resultados foram alcançados pelo algoritmo RF, utilizando todos atributos (sem compressão) e uma amostragem de undersampling nos dados de treino. Este modelo obteve o melhor valor global da área da curva Receiver Operating Characteristic (ROC), correspondendo a uma discriminação de qualidade (valor da mediana da área de 87consegue realizar previsões atempadas de elevada qualidade para quatro classes (A, B, C e D) e de qualidade razoável para a classe restante (E)

Human skeleton detection from semi-constrained environment video

The correct classification of human skeleton from video is a key issue for the recognition of human actions and behavior. In this paper, we present a computational system for a passive detection of human star skeleton from raw video. The overall system is based on two main modules: segmentation and star skeleton detection. For each module, several computer vision methods were adjusted and tested under a comparative analysis that used a challenging video dataset (e.g., different daylight and weather conditions). The obtained results show that our system is capable of detecting human skeletons in most situations.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013 and research grant FCT SFRH/BD/84939/2012.info:eu-repo/semantics/publishedVersio

Multi-Criteria Decision Making Model for Application Maintenance Offshoring Using Analytic Hierarchy Process

The phenomenon of Global Software Development (GSD) has attracted the interest of businesses all over the world. It brings together partners from various national and corporate cultures to develop applications with numerous advantages, including access to a vast labor pool, cost savings, and round the clock growth. GSD, on the other hand, is technologically and organizationally diverse and poses a number of obstacles for the development team, such as geographical distance, cultural differences, communication and language barriers. Global services are provided by selecting one of the suitable global delivery options, i.e., the onshore model, nearshore model or offshore model. Experts typically choose one of the models based on the nature of the project and the needs of the customer. However, the vendors and clients lack an adequate decision support system that can assist them in making suitable sourcing decisions. Therefore, the current study presents a Multi-Criteria Decision Making (MCDM) model for offshore outsourcing decisions of application maintenance. To achieve our target, two systematic literature reviews were conducted that explored a list of 15 influencing factors. The identified factors were further evaluated in the outsourcing industry by performing an empirical study that resulted in a list of 10 critical success factors. We propose a sourcing framework based on the critical success factors that can assist decision makers in adopting a suitable sourcing strategy for the offshore outsourcing of application maintenance. In order to further enhance the decision-making process, the MCDM model is developed based on the Analytic Hierarchy Process (AHP). The MCDM model is evaluated with three case studies in highly reputable international companies, including IBM Stockholm, Sweden, Vattenfall AB, Stockholm, Sweden and a London based company in the United Kingdom. The outcomes of these case studies are further reviewed and validated by the outsourcing specialists in other firms. The proposed model is used as a decision support system that determines the ranking of sourcing alternatives and suggests the most suitable option for application maintenance offshoring

OffshoringDSS: An Automated Tool of Application Maintenance Offshoring

The rapid spread of the internet over the last two decades has prompted more and more companies to deploy their work internationally. The offshoring strategy enables organizations to cut down costs, boost shareholder value, acquire a competitive advantage, reduce cycle time, increase workforce flexibility, generate revenue and focus on their core business. The number of worldwide software development projects has increased due to globalization. Global Software Development (GSD) projects are forecast to grow by 20% to 30% in countries like India and China. The outsourcing experts choose one of the suitable models from the available global delivery options to deliver services in the global software paradigm. However, adopting the appropriate model for application maintenance is a complicated process. In addition, the right model is selected based on various influencing factors, type of the project and client requirements. Additionally, sufficient domain expertise is necessary for the decision making of offshore outsourcing. Currently, there is no dynamic and automated tool for the decision making of application maintenance offshoring. Therefore, this study presents an Offshoring Decision Support System (OffshoringDSS), an automated and novel tool to make the offshoring decisions of application maintenance. The suggested tool is based on the Analytic Hierarchy Process (AHP) technique. The tool automatically performs all the calculations involved in the decision making and ranks the sourcing models

OffshoringDSS: An Automated Tool of Application Maintenance Offshoring

The rapid spread of the internet over the last two decades has prompted more and more companies to deploy their work internationally. The offshoring strategy enables organizations to cut down costs, boost shareholder value, acquire a competitive advantage, reduce cycle time, increase workforce flexibility, generate revenue and focus on their core business. The number of worldwide software development projects has increased due to globalization. Global Software Development (GSD) projects are forecast to grow by 20% to 30% in countries like India and China. The outsourcing experts choose one of the suitable models from the available global delivery options to deliver services in the global software paradigm. However, adopting the appropriate model for application maintenance is a complicated process. In addition, the right model is selected based on various influencing factors, type of the project and client requirements. Additionally, sufficient domain expertise is necessary for the decision making of offshore outsourcing. Currently, there is no dynamic and automated tool for the decision making of application maintenance offshoring. Therefore, this study presents an Offshoring Decision Support System (OffshoringDSS), an automated and novel tool to make the offshoring decisions of application maintenance. The suggested tool is based on the Analytic Hierarchy Process (AHP) technique. The tool automatically performs all the calculations involved in the decision making and ranks the sourcing models