SUSPICIOUS ACTIVITY DETECTION
description
Transcript of SUSPICIOUS ACTIVITY DETECTION
SUSPICIOUS ACTIVITY DETECTION
Student: Dane Brown 2713985
Supervisor : James ConnanCo-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 DATETesting 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
QUESTIONS AND ANSWERSThank You!