Transcript Suspicious Activity Detection - University of the Western Cape

Student: Dane Brown 2713985
Supervisor : James Connan
Co-Supervisor : Mehrdad Ghaziasgar
OVERVIEW

INTRODUCTION

USER INTERFACE CHANGES

DESIGN DECISIONS

IMPLEMENTATION

TOOLS USED

PROJECT PLAN

DEMO
INTRODUCTION
 What
does the system regard as normal activity
 Park car, get out, walk away, get back in, drive away
 What
does the system regard as suspicious?
 Loitering next to a vehicle is suspicious
USER INTERFACE CHANGES
DESIGN DECISIONS
 Haar
feature-extraction
 Typically the training 1000+ sample frames containing
normal activity and suspicious activity
 What
not haar feature-extraction?
 Performance is good only on a very fast machine
 There are simpler and more robust ways to differentiate
suspicious and normal behaviour.
IMPLEMENTATION

Gray Scale and Frame differencing
IMPLEMENTATION cont.

Thresholding and Motion History Image (MHI)
IMPLEMENTATION cont.

Blob and movement detection
IMPLEMENTATION cont.

Suspicious activity detected!
TOOLS USED cont.
Kubuntu 10.04
 Opencv with ffmpeg – video manipulation
 VirtualDub – open source video editor

PROJECT PLAN
GOAL
DUE DATE
Testing
Till the end of term 4
Hand in term 3 Documentation
15 September 2010
Final Demo and Final Documentation
End of term 4
REFERENCES

Davis, J. W. (2005). Motion History Image. Retrieved 2010,
from The Ohia State University.

Bouakaz, S. (2003). Image Processing and Analysis
Reference. Retrieved 2010, from Université Claude Bernard
Lyon 1.

Green, B. (2002). Histogram, Thresholding and Image
Centroid Tutorial. Retrieved 2010, from Drexel University
site.
DEMO

1.Introduction – normal car driving
past


2. Normal activity – typical drive away
3. Suspicious – Two men loitering