Athanasios Voulodimos

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In this issue's Works in Progress department, we have six projects. The first two projects address an individual's privacy concerns and preferences. The next entry discusses a project on data protection for electronic passports. The remaining three projects are investigating various types of privacy protection mechanisms for data collected in pervasive(More)
In this paper, we propose a novel online framework for behavior understanding, in visual workflows, capable of achieving high recognition rates in real-time. To effect online recognition, we propose a methodology that employs a Bayesian filter supported by hidden Markov models. We also introduce a novel re-adjustment framework of behavior recognition and(More)
The emergence of cloud environments has made feasible the delivery of Internet-scale services by addressing a number of challenges such as live migration, fault tolerance and quality of service. However, current approaches do not tackle key issues related to cloud storage, which are of increasing importance given the enormous amount of data being produced(More)
In this paper, we propose a novel system for visual recognition and summarization of pick and place tasks that may be executed in settings such as an industrial assembly line. Our novel approach is based on the utilization of hidden Markov models for online task recognition as well as on the use of prior knowledge via a Hopfield-based optimization scheme.(More)
Modelling and classification of time series stemming from visual workflows is a very challenging problem due to the inherent complexity of the activity patterns involved and the difficulty in tracking moving targets. In this paper, we propose a framework for classification of visual tasks in industrial environments. We propose a novel method to(More)
In this paper we introduce the WR (Workflow Recognition) dataset. Recorded in the production line of a major automobile manufacturer, this dataset consists of sequences that depict workers executing industrial workflows. The heavy occlusions, outliers, the visually complicated background and the human-machinery interaction are among the factors that make(More)
B ehavior recognition in video is a focal point of research in the computer vision, image processing, and multimedia communities. Driven by applications such as assistive technologies, security, intelligent transportation, and human-computer interaction, a considerable body of work targets hierarchical event detection, workflow monitoring, and structured(More)
In this paper we propose a method to enhance activity recognition in complex environments, where problems like occlusions, noise and illumination changes are present. In order to address the problems induced by the dependency on the camera’s viewpoint, multiple cameras are commonly used in an endeavour to exploit redundancies. We initially examine the(More)
Human behavior recognition and real world environments monitoring constitute challenging research problems rapidly gaining momentum over the last years. Methods for time series classification like the Hidden Markov Models have been employed in the past for similar tasks, however in many challenging cases they fail, since some behaviors are much more(More)
In this work, we propose a framework for classifying structured human behavior in complex real environments, where problems such as frequent illumination changes and heavy occlusions are expected. Since target recognition and tracking can be very challenging, we bypass these problems by employing an approach similar to Motion History Images for feature(More)