1. Student: Dane Brown 2713985 Supervisor : James Connan Co-Supervisor : Mehrdad Ghaziasgar

2. OVERVIEW  INTRODUCTION  USER INTERFACE CHANGES  DESIGN DECISIONS  IMPLEMENTATION  TOOLS USED  PROJECT PLAN  DEMO

3. 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

4. USER INTERFACE CHANGES

5. 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.

6. IMPLEMENTATION  Gray Scale and Frame differencing

7. IMPLEMENTATION cont.  Thresholding and Motion History Image (MHI)

8. IMPLEMENTATION cont.  Blob and movement detection

9. IMPLEMENTATION cont.  Suspicious activity detected!

10. TOOLS USED cont.  Kubuntu 10.04  Opencv with ffmpeg – video manipulation  VirtualDub – open source video editor

11. PROJECT PLAN GOALDUE DATE Testing Till the end of term 4 Hand in term 3 Documentation15 September 2010 Final Demo and Final DocumentationEnd of term 4

12. 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.

13. DEMO  1.Introduction – normal car driving past  2. Normal activity – typical drive away  3. Suspicious – Two men loitering