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!