Ideas
Seminar: Richard Souvenir
Date:
August 23, 2013
Prof.
Richard Souvenir and his
students focused on computer vision and machine learning in particular,
exploring ways to coordinate multiple cameras, processing the information in
one or more computers to understand human motion and behavior.
First
of all, Computer vision is concerned with modeling and replicating human vision
using computer software and hardware. It seems combines knowledge in computer
science, and many other disciplines such as electrical engineering, mathematics,
physiology, biology, and cognitive science. It needs knowledge from all these
fields in order to understand and simulate the operation of the human vision
system. I think Images and movies are everywhere, computer vision is becoming more
popular as the power of computers increase. Computer vision is concerned with
the automatic extraction, analysis and understanding of useful information from
a single image or a sequence of image. It involves the development of a theoretical
and algorithmic basis to achieve automatic visual understanding. And they have
been studying how to reconstructed, interpret and understand a 3D scene from
its 2D images in terms of the properties of the structure present in the scene
Especially, during the lecture, they showed us to
present computer vision to detect and track the face and hands of a human being
in real time from a video sequence captured by a webcam. Tracking people in
video enables applications in surveillance, traffic monitoring, and video
conferencing. They said, recent multi-camera methods have helped to overcome
some of the issues associated with object tracking, such as drift and occlusion
that arise in the single-camera case. However, the integration of multiple
cameras introduces new challenges in terms of resource consumption and
algorithm complexity. Moreover, while tracking accuracy tends to increase with
the number of cameras, the potential also increases for poor measurements from
individual cameras to negatively act aggregate tracking estimates. Of course
computer vision and machine learning will be definitely helpful to understand
spatial cognition and people reaction.
No comments:
Post a Comment