An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

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.

Videovigilância nos espaços públicos e criminalidade contra o Património: Santuário de Fátima

Dissertação de mestrado em Crime, Diferença e Desigualdade

Evaluating ubiquitous computing environments using 3D simulation

Human activity is very dynamic and subtle, and most physical environments are also highly dynamic and support a vast range of social practices that do not map directly into any immediate ubiquitous computing functionally. Identifying what is valuable to people is very hard and obviously leads to great uncertainty regarding the type of support needed and the type of resources needed to create such support. We have addressed the issues of system development through the adoption of a Crowdsourced software development model [13]. We have designed and developed Anywhere places, an open and flexible system support infrastructure for Ubiquitous Computing that is based on a balanced combination between global services and applications and situated devices. Evaluation, however, is still an open problem. The characteristics of ubiquitous computing environments make their evaluation very complex: there are no globally accepted metrics and it is very difficult to evaluate large-scale and long-term environments in real contexts. In this paper, we describe a first proposal of an hybrid 3D simulated prototype of Anywhere places that combines simulated and real components to generate a mixed reality which can be used to assess the envisaged ubiquitous computing environments [17].

O Sinclinal de ferro de Torre de Moncorvo: um recurso mineral como catalisador de um Tempolntermitente

Dissertação de mestrado integrado em Arquitectura

Forecasting human entrances at a commercial store using facial recognition data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

PEGylated ofloxacin nanoparticles render strong antibacterial activity against many clinically important human pathogens.

The rise of bacterial resistance against important drugs threatens their clinical utility. Fluoroquinones, one of the most important classes of contemporary antibiotics has also reported to suffer bacterial resistance. Since the general mechanism of bacterial resistance against fluoroquinone antibiotics (e.g. ofloxacin) consists of target mutations resulting in reduced membrane permeability and increased efflux by the bacteria, strategies that could increase bacterial uptake and reduce efflux of the drug would provide effective treatment. In the present study, we have compared the efficiencies of ofloxacin delivered in the form of free drug (OFX) and as nanoparticles on bacterial uptake and antibacterial activity. Although both poly(lactic-co-glycolic acid) (OFX-PLGA) and methoxy poly(ethylene glycol)-b-poly(lactic-co-glycolic acid) (OFX-mPEG-PLGA) nanoformulations presented improved bacterial uptake and antibacterial activity against all the tested human bacterial pathogens, namely, Escherichia coli, Proteus vulgaris, Salmonella typhimurium, Pseudomonas aeruginosa, Klebsiella pneumoniae and Staphylococcus aureus, OFX-mPEG-PLGA showed significantly higher bacterial uptake and antibacterial activity compared to OFX-PLGA. We have also found that mPEG-PLGA nanoencapsulation could significantly inhibit Bacillus subtilis resistance development against OFX.

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores