Abstract: This study discusses a Turkish music education model
similar to its Venezuelan counterpart El Sistema, in which
socialization and human development are the main goals. The Music
for Peace (Baris Icin Muzik) model, founded in 2005 by an idealist
humanitarian in Istanbul, started as a pilot project with accordion and
today makes symphonic music education. The program aims to offer
social change through free-of-charge. In such a big city like Istanbul, in a deprived inner city center
people have poor economic, social and cultural conditions. In that
Edirnekapi district people don’t have opportunities to join the cultural
and social life, like music or sports. It is believed that this initiative
covered a part of this gap by giving children the opportunities to
participate in social and cultural life. In this study it is planned to understand what social changes could
music education could make in children’s lives. In the complimentary
music lessons children works in groups, which helps them to learn
the feelings of solidarity, friendship, communion and sharing. By Music for Peace project children connect with the community,
they have the belief to succeed in life because they feel that they are
loved by their friends, instructors and families. In short they feel that
they are important, thus brings the success in life. Additionally, it is
believed that, this program has achieved success. Today
approximately 400 children participate in this programs orchestras
and choirs. Some of the students get into the conservatories. And the
center is not just a place where they get music lessons but also a place
where they get socialized. And music education helps children to
have strong sense of identity, self-confidence and self-esteem.
Abstract: Recognizing human action from videos is an active
field of research in computer vision and pattern recognition. Human
activity recognition has many potential applications such as video
surveillance, human machine interaction, sport videos retrieval and
robot navigation. Actually, local descriptors and bag of visuals words
models achieve state-of-the-art performance for human action
recognition. The main challenge in features description is how to
represent efficiently the local motion information. Most of the
previous works focus on the extension of 2D local descriptors on 3D
ones to describe local information around every interest point. In this
paper, we propose a new spatio-temporal descriptor based on a spacetime
description of moving points. Our description is focused on an
Accordion representation of video which is well-suited to recognize
human action from 2D local descriptors without the need to 3D
extensions. We use the bag of words approach to represent videos.
We quantify 2D local descriptor describing both temporal and spatial
features with a good compromise between computational complexity
and action recognition rates. We have reached impressive results on
publicly available action data set