Researching the Basics

I started reading the following papers:

• Periodic Motion Detection and Segmentation via Approximate Sequence Alignment; Laptev, Belongie, Perez, Wills

• Robust periodic motion and motion symmetry detection; Cutler, Davis

I jumped to the Cutler/Davis paper simply because the Laptev paper referred to it multiple times. I found it useful for building a simple foundation for understanding periodic motion.

Based on the Cutler/Davis paper, w+e begin defining periodic motion for a given pixel as: At time t, a pixel point will repeat itself again at time t+p. There may be a translation, T(.), of the point at time t.

(1)

We restrict our definition of the periodic value, p, to be the smallest possible value. From this, we can define the frequency as:

(2)

A radar period, p, is a constant for each radar. However each radar has a different period.
For most cases, if there are multiple radars on a ship, they will not have the same rotation period. This is because when there are 2 or more radars, they are using different frequency bands. Each radar frequency band has a different physical size and rotation period.

From that deduction I may be able to make an assumption that if there are multiple radars in the field of view, they will not have the same period.

for (3)

Equation (3) may come in handy when we need to distinguish from multiple radars.

Stabilization
My first phase now is to stabilized the video sequence. In order to tackle this I will assume there is only translation motion. There are several methods to tackle this problem.

First I'll try doing this manually to get a good understanding of what's going on. This will be done by points on the image then finding corresponding points in successive frames. This will verify that the translation model assumption is valid.

Vincent Rabaud suggested looking into the following methods for feature tracking:
KLT: http://www.ces.clemson.edu/~stb/klt/
Black's : http://www.cs.brown.edu/~black/framework.html

Black's uses optical flow which will more than likely be expensive to compute. Therefore I will first look into the KLT (Kanade-Lucas-Tomasi) algorithm.