Back to Search
Start Over
Multimedia Content Analysis for Event Detection
- Publication Year :
- 2015
-
Abstract
- The wide diffusion of multimedia contents of different type and format led to the need of effective methods to efficiently handle such huge amount of information, opening interesting research challenges in the media community. In particular, the definition of suitable content understanding methodologies is attracting the effort of a large number of researchers worldwide, who proposed various tools for automatic content organization, retrieval, search, annotation and summarization. In this thesis, we will focus on an important concept, that is the inherent link between ''media" and the ''events" that such media are depicting. We will present two different methodologies related to such problem, and in particular to the automatic discovery of event-semantics from media contents. The two methodologies address this general problem at two different levels of abstraction. In the first approach we will be concerned with the detection of activities and behaviors of people from a video sequence (i.e., what a person is doing and how), while in the second we will face the more general problem of understanding a class of events from a set visual media (i.e., the situation and context). Both problems will be addressed trying to avoid making strong a-priori assumptions, i.e., considering the largely unstructured and variable nature of events.As to the first methodology, we will discuss about events related to the behavior of a person living in a home environment. The automatic understanding of human activity is still an open problems in the scientific community, although several solutions have been proposed so far, and may provide important breakthroughs in many application domains such as context-aware computing, area monitoring and surveillance, assistive technologies for the elderly or disabled, and more. An innovative approach is presented in this thesis, providing (i) a compact representation of human activities, and (ii) an effective tool to reliably measure the similarity b
Details
- Database :
- OAIster
- Notes :
- application/pdf
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1137087424
- Document Type :
- Electronic Resource