Human Activity Recognition is an actively researched domain for the past few
decades, and is one of the most eminent applications of today. It is already part of our life,
but due to high level of uncertainty and challenges of human detection, we have only
application specific solutions. Thus, the problem being very demanding and still remains
Within this PhD we delve into the problem, and approach it from a variety of viewpoints.
At start, we present and evaluate different architectures and frameworks for activity
Henceforward, the focal point of our attention is automatic human activity
recognition. We conducted and present a survey that compares, categorizes, and evaluates
research surveys and reviews into four categories.
Then a novel fully automatic view-independent multi-formal languages
collaborative scheme is presented for complex activity and emotion recognition, which is
the main contribution of this dissertation.
We propose a collaborative three formal-languages, that is responsible for parsing
manipulating, and understanding all the data needed. Artificial Neural Networks are used
to classify an action primitive (simple activity), as well as to define change of activity.
Finally, we capitalize the advantages of Fuzzy Cognitive Maps, and Rule-Based Colored
Petri-Nets to be able to classify a sequence of activities as normal or ab-normal.