Persistent Surveillance Tracking In Persistent Surveillance Adam Mounts Computer Systems Lab 2009-2010

Abstract Crucial development in security and/or surveillance systems. Useful in the event of a crisis situation to follow potential suspects or targets All based on aerial imagery. Has an advantage over human analysts in efficiency and feasibility

Introduction The goal of this project is to create and compare trackers that can follow a certain object, whether it be a human, a vehicle, or some other moving target, and trace its path through a series of images. http://www.stdb-seesamples.com/seesamples_pdfs/Aerial%20Image.jpg

Background Project Chloe www.homelandsecurity.org/StakeholdersMay07/Br45_Wilson.pdf

The first aspect of this project was the creation of a “movie maker” that creates and saves a sequence of images that can be used as data for a quick test of the tracking algorithms. A small black circle moves across the screen randomly, and frames are saved into files. Testing Software

With new data, a closer simulation can be performed.

The first algorithm I use to track a target is the use of pixel subtraction. This essentially takes a series of linear images, and compares them pixel by pixel and highlights the differences between them. Pixel Subtraction

Pros and Cons Relatively easy to code Effective and quick in simple situations Slow and inaccurate in complex scenarios Not as accurate as other algorithms

In Action

Kalman Filter A Kalman Filter is a recursive algorithm that repetitively minimizes the square of the error, after making a slight random adjustment to the data. By doing this, its estimate gradually approaches the actual answer. By using variables such as location and velocity, it can be applied to tracking.

Kalman Filter in Work

Error Minimization

As shown in some of my previous slides, these are the current outputs of my programs Results