| Περίληψη: | It is undoubtable that if machines could automatically interpret the human presence and movement in our everyday life, many tasks would be transposed and revolutionized. Multiple human tracking (MHT), encompassing human presence and movement analysis, are key components in many computer vision applications, in which the fundamental goal is to automatically segment, capture, recognize and analyze human activities in both controlled and uncontrolled environments.
One of the core topics the thesis deals with refers to the research and development of a robust human detection and tracking framework, which exploits the use of privacy-preserving information for supporting emerging application scenarios in the Energy Efficiency domain such as occupancy-driven facility management and the utilization of calibrated occupancy models for building performance simulation and prediction, which are tools increasingly used by the architecture, engineers and construction community at design, refurbishment and operational phases of a building lifecycle.
Furthermore, as of today, an increasing number of perimeter security and surveillance firms are now delivering multiple-sensor threat identification and classification toolkits that can recognize unwarranted and suspicious activity in fenced areas. The trend is to introduce cost-effective solutions coupled with privacy-preserving multi-sensorial networks, to further reduce high false alarm rates as well as to research and develop techniques that can interpret activities of moving objects in the perimeter and robust identify suspicious activity, taking into account minimal false alarm rates and operators burden.
Within this thesis, a novel framework for the automatic recognition of suspicious activities in outdoor infrared-based video surveillance is presented, in which its discriminating power in classifying high risk activities like trespassing from zero risk activities such as loitering outside the perimeter is outlined. To cope with human presence and movement in outdoor environments, an extended version of the multiple-human tracking framework introduced for controlled environments (i.e. buildings) is employed as an important step in analyzing human behavior in non-controlled environments, fully exploiting privacy-preserving information from infrared video sequences.
During this thesis, an extensive experimental evaluation of the proposed methodologies for human detection, tracking and movement analysis in indoor and outdoor environments has been carried out, which effectively illustrated the robustness of the proposed algorithms and their applicability in a range of emerging application scenarios related to human behavior analysis in buildings, energy efficiency in buildings and critical infrastructure perimeter protection.
The thesis is concluded with an in-depth discussion summarizing the major achievements of the current work, as well as identifying open issues, limitations and research challenges that could be addressed in future works.
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