Student Research Projects
Supervisor
Li Cheng, Ajunct Research Fellow (ANU) & Researcher, Statistical
Machine Learning Program (NICTA)
These projects are suitable as:
summer
research projects
honours thesis projects
Motion detection
Project Code: SML_21
How to detect moving objects automatically and in real time from, for
example, a hand-held camera or a robot? You will have opportunity to
explore exciting computer vision (for example, SIFT detector from UBC)
and machine learning techniques for a sensible solution. In this
project, you will also enjoy playing with these robots
(www.webcam32.com/SRV_info.html).
It will involve coding in C/C++, and require good knowledge of linear
algebra, and some working knowledge of image/video processing.
Supervision will be provided on a day-to-day basis.
Action recognition
Project Code: SML_22
Given a video sequence of human activities, it is easy for us to tell
whether a person is walking, running, or dancing. The aim of this
project is to teach a computer to recognize human actions, that is, we
predefine a set of actions that reasonably cover over the possible
human activities in the obtained and incoming video sequences. Then the
computer will learn to recognize these actions from a new video
sequence.
This turns out to be rather difficult for computer vision, mainly due
to different viewing angles, various poses within and across the
category of actions. Nevertheless it attracts a lot of attentions in
recent years, see www.cs.berkeley.edu/projects/vision/action for a
collection of related research papers.
In this project, you are expect to explore many interesting computer
vision (eg. shape analysis) and machine learning (eg. probabilistic
models) techniques.
The summer scholar is expected to be confident in C/C++ and python
programming, as well as have appropriate mathematics training (linear
algebra, probability and statistics). Some working knowledge of
image/video processing will be beneficial.
The summer scholar will work closely with the supervisor on a
day-to-day basis.
Novelty detection from video data
Project Code: SML_23
Modern technologies enable us to easily obtain tons of video sequences
everyday. Usually the recorded scenarios are similar things that repeat
over the time. For example, from a live traffic control camera, we
might observe vehicles running along the lanes most of the time.
However, there are occasions when certain vehicle break the traffic
law, and these are the situations that drivers and traffic officers
have to pay attention to. Human is capable of monitoring and figuring
out these novel situations, however, no one can do this consistently 24
hours a day, 365 days a year. This project aims at enabling computer to
automatically detect these situations.
This problem is very important to, eg., surveillance industry and have
potentials for various applications. Many efforts have been taken over
the years, the solutions are still far from being satisfactory. To have
a flavor, here is one related paper:
---- O. Boiman, M. Irani, Detecting Irregularities in Images and
in Video. International Conference on Computer Vision
(ICCV), Beijing, 2005.
During this project, the summer student will have expose to various
statistical machine learning methods, such as Bayesian method and
kernel method, as well as recent image/video processing techniques.
The summer scholar is expected to be confident in C/C++ and python
programming, and have appropriate mathematics training (linear algebra,
probability and statistics). Some prior knowledge of image/video
processing will be beneficial.
The summer scholar will work closely with the supervisor on a
day-to-day basis.