SUSPICIOUS ACTIVITY DETECTION

1. Student: Dane Brown 2713985 Supervisor : James Connanand MehrdadGhaziasgar SUSPICIOUS ACTIVITY DETECTION

2. OVERVIEW • INTRODUCTION • DESIGN DECISIONS • IMPLEMENTATION • PROJECT PLAN • DEMO

3. INTRODUCTION • Extremely high crime rate in South Africa • Car break-in rate was 16000 in 2009 • 18 times the rate of USA • Carjacking is the most common crime in South Africa • Costing tax payers billions of rands!

4. INTRODUCTION cont.

5. INTRODUCTION cont. • CCTV cameras • Human monitored • Current solution ineffective • Continued high break-in rate

6. INTRODUCTION cont. • Pioneered revolutionary system • Uses computer vision techniques • Automatically detects suspicious activity from a video feed • Detection happens in real-time

7. INTRODUCTION cont. • Pioneered revolutionary system

8. DESIGN DECISIONS • Classification methods • Machine learning such as Haar-like features with Adaboost • Generally training 2000+ sample frames • Why not a classification method? • Trade-off between speed, complexity and accuracy • There are simpler and more robust ways to differentiate suspicious and normal behaviour.

9. IMPLEMENTATION • Original frame in RGB colour

10. IMPLEMENTATION cont. • Gray Scale and Frame differencing

11. IMPLEMENTATION cont. • Motion History Image (MHI)

12. IMPLEMENTATION cont. • Blob and movement detection (using MHI)

13. IMPLEMENTATION cont. • Blob and movement detection

14. IMPLEMENTATION cont. • Blob and movement detection

15. IMPLEMENTATION cont. • System determines normal activity • Park car

16. IMPLEMENTATION cont. • System determines normal activity • Park car

17. IMPLEMENTATION cont. • System determines normal activity • Get out

18. IMPLEMENTATION cont. • System determines normal activity • Walk away

19. IMPLEMENTATION cont. • System determines normal activity • Walk away

20. IMPLEMENTATION cont. • System determines normal activity • Get back in

21. IMPLEMENTATION cont. • System determines normal activity • Drive away

22. IMPLEMENTATION cont. • System determines normal activity • Drive away

23. IMPLEMENTATION cont. • System determines suspicious activity • Loitering next to a vehicle is suspicious

24. IMPLEMENTATION cont. • System determines suspicious activity • Loitering next to a vehicle is suspicious

25. IMPLEMENTATION cont. • System determines suspicious activity • Loitering next to a vehicle is suspicious

26. IMPLEMENTATION cont. • System determines suspicious activity • Loitering next to a vehicle is suspicious

27. IMPLEMENTATION cont. • System determines suspicious activity • Loitering next to a vehicle is suspicious

28. IMPLEMENTATION cont. • System determines suspicious activity • Loitering next to a vehicle is suspicious

29. IMPLEMENTATION cont. • System determines other suspicious activity • Parking, but not leaving the vehicle

30. IMPLEMENTATION cont. • System determines other suspicious activity • Accelerating too fast

31. IMPLEMENTATION cont. • Suspicious activity detected!

32. DEMO • 1. Normal activity - typical drive away • 2. Suspicious - two men loitering • 3. Suspicious - Stationary • 4. Suspicious - Acceleration

33. REFERENCES • Davis, J. W. (2005). Motion History Image. Retrieved 2010, from The OhiaState University. • Green, B. (2002). Histogram, Thresholding and Image Centroid Tutorial. Retrieved 2010, from Drexel University site. • Trip Atlas. (2010). Retrieved from Carjacking: http://tripatlas.com/Carjacking#South%20Africa • Hijacking. (2010). Retrieved from Arrive Alive: http://www.arrivealive.co.za/pages.aspx?i=2364

34. QUESTIONS AND ANSWERSThank You!