Name: Douglas Almonfrey
Type: PhD thesis
Publication date: 26/07/2018

Namesort descending Role
Evandro Ottoni Teatini Salles Co-advisor *
Raquel Frizera Vassallo Advisor *

Examining board:

Namesort descending Role
Evandro Ottoni Teatini Salles Co advisor *
Joao Marques Salomao External Examiner *
José Alberto Rosado Santos-Victor External Examiner *
Patrick Marques Ciarelli Internal Examiner *
Raquel Frizera Vassallo Advisor *
Thomas Walter Rauber External Examiner *

Summary: The topic of intelligent spaces has experienced increasing attention in the last decade. As an
instance of the ubiquitous computing paradigm, the general idea is to extract information from
the ambient and use it to interact and provide services to the actors present in the environment.
The sensory analysis is mandatory in this area and humans are usually the principal actors
involved. In this sense, we propose a human detector to be used in an intelligent space based on
a multi-camera network. Our human detector is implemented using concepts of cloud computing
and service-oriented architecture (SOA). As the main contribution of the present work, the
human detector is designed to be a service that is scalable, reliable and parallelizable. It is
also a concern of our service to be flexible, decoupled from specific processing nodes of the
infrastructure and less structured as possible, attending different intelligent space applications
and services. Since it can be easily found already installed in many different environments,
a multi-camera system is used to overcome some difficulties traditionally faced by existing
human detection methods that are based in only one camera. To validate our approach, we
implement three different applications that are proof of concept (PoC) of many day-to-day real
tasks. Two of these applications are related to robot navigation and demand the knowledge about
the tridimensional localization of the humans present in the environment. With respect to time
and detection performance requirements, our human detection service has proved to be suitable
for interacting with the other services of our Intelligent Space, in order to successfully complete
the tasks of each application. As an additional contribution, a feature extraction procedure based
on the independent component analysis (ICA) theory is proposed as part of a detector and
evaluated in public datasets. The pedestrian detection area is used as a playground to develop
the human detector since it is the most mature research area of the human detection literature.
The resulted detector is also used in the pipeline of the proposed human detection service, thus,
being also applied in real-time applications in the intelligent space used as our testbed.

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