SUSPICIOUS ACTIVITY DETECTION
Student: Dane Brown 2713985
Supervisor : James Connan and Mehrdad Ghaziasgar
OVERVIEW INTRODUCTION
DESIGN DECISIONS
IMPLEMENTATION
PROJECT PLAN
DEMO
INTRODUCTION Extremely high crime rate in South Africa
Car break-in rate was 16000 in 200918 times the rate of USACarjacking is the most common crime in South AfricaCosting tax payers billions of rands!
INTRODUCTION cont.
2006 2007 2008 200913000
13500
14000
14500
15000
15500
16000
16500
Carjackings 2006-2009
INTRODUCTION cont. CCTV cameras
Human monitoredCurrent solution ineffectiveContinued high break-in rate
INTRODUCTION cont. Pioneered revolutionary system
Uses computer vision techniquesAutomatically detects suspicious activity from a
video feedDetection happens in real-time
INTRODUCTION cont. Pioneered revolutionary system
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.
IMPLEMENTATION Original frame in RGB colour
IMPLEMENTATION cont. Gray Scale and Frame differencing
IMPLEMENTATION cont. Motion History Image (MHI)
IMPLEMENTATION cont. Blob and movement detection (using MHI)
IMPLEMENTATION cont. Blob and movement detection
IMPLEMENTATION cont. Blob and movement detection
IMPLEMENTATION cont. System determines normal activity
Park car
IMPLEMENTATION cont. System determines normal activity
Park car
IMPLEMENTATION cont. System determines normal activity
Get out
IMPLEMENTATION cont. System determines normal activity
Walk away
IMPLEMENTATION cont. System determines normal activity
Walk away
IMPLEMENTATION cont. System determines normal activity
Get back in
IMPLEMENTATION cont. System determines normal activity
Drive away
IMPLEMENTATION cont. System determines normal activity
Drive away
IMPLEMENTATION cont. System determines suspicious activity
Loitering next to a vehicle is suspicious
IMPLEMENTATION cont. System determines suspicious activity
Loitering next to a vehicle is suspicious
IMPLEMENTATION cont. System determines suspicious activity
Loitering next to a vehicle is suspicious
IMPLEMENTATION cont. System determines suspicious activity
Loitering next to a vehicle is suspicious
IMPLEMENTATION cont. System determines suspicious activity
Loitering next to a vehicle is suspicious
IMPLEMENTATION cont. System determines suspicious activity
Loitering next to a vehicle is suspicious
IMPLEMENTATION cont. System determines other suspicious activity
Parking, but not leaving the vehicle
IMPLEMENTATION cont. System determines other suspicious activity
Accelerating too fast
IMPLEMENTATION cont. Suspicious activity detected!
1. Normal activity - typical drive away
2. Suspicious - two men loitering
3. Suspicious - Stationary
4. Suspicious - Acceleration
DEMO
REFERENCES Davis, J. W. (2005). Motion History Image. Retrieved 2010,
from The Ohia State 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
QUESTIONS AND ANSWERSThank You!