Transcript Temporal Effects of Singularities and Event Horizons

ECE 172A
SIMPLE OBJECT DETECTOR WITH INDICATOR
WHEN A NEW OBJECT HAS BEEN ADDED TO
OR MISSING IN A ROOM
Presented
by
Hugo Groening
INTRODUCTION
In the security industry, there has always been the
demand for simple, accurate and economical systems
that allow the prevention or ability to track through
footage when a security breach has occurred. The
project to be presented targets locations such as
museums, stores, homes, Banks, etc.
AGENDA

Objective

Related Research

Method

Results

Future work or improvements

Conclusion
Objective
-
To create an object recognition and detector in a room using AVI
files.
To train detector to indicate when an object is missing or possibly
about to be taken.
Related Work and Research
Industry
- Geovision: http://www.geovision.com.tw/demo/vid/demo4.htm
References:
-
A. Torralba, K. P. Murphy and W. T. Freeman. (2004). "Sharing features: efficient boosting
procedures for multiclass object detection". Proceedings of the 2004 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (CVPR). pp 762- 769
-
B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: a database and webbased tool for image annotation. Technical Report AIM-2005-025, MIT AI Lab Memo,
September, 2005.
Can be found at: http://people.csail.mit.edu/brussell/research/AIM-2005-025-new.pdf
Location of Code and Database:
http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html
Goal
Edited from http://www.geovision.com.tw/demo/vid/demo4.htm, Geovision Inc.
Method
Initialize Video output
Choose Video to be analyzed
Look at 1st frame to find size
loops into frames
Detect if object count changes from previous frame
and makes copy of current
frame
If different
Count Objects in frame
WARNING
Change to Grey and invert to BW
Erodes, dilates
Find centroid of objects and
tracks them
Nothing change, keeps counting
Results
-Count Objects
-Find Objects
-Warns when object count changes
-Storages Video Output for Future analysis
Limitations
-
Hard drive Space for memory allocation of Database
Processor
AVI files taken from other video cameras
Objects and background color
Time frame
Output Footage
Future Work or Improvements
-
Minimize the memory size of Database
Funding for continuation of Labelme Database
Access to individual Objects within Database
Research other Algorithm options for Object detection and
Classification
Find more adequate video Recorder for surveillance purposes
Live Object tracking detector
To become more knowledgeable with Matlab and other
Programming languages.
Conclusion
The background and knowledge obtained through the practice of the
information previously researched, had developed a strong
awareness of how digital image processing can be directly applied
to needs in society. In even the most simple cases, such as in the
security or surveillance industry, high end technology will be needed
for accuracy. Continuous development and improvements will also
be necessary.