The above video is from stabilizing and differencing a ship with radar video sequence. The motion of the radar is noticed by the vertical edges of the spinning armature.
Keeping in mind the findings from the previous post, let's take a horizontal slice of each frame instead of vectorizing the whole frame. I'm going to cheat by taking a scanline that directly overlaps where the radar is located which is the 204th row. Below is a snapshot of a video frame with a yellow box indicating the general region of the scanline location.
Keeping in mind the findings from the previous post, let's take a horizontal slice of each frame instead of vectorizing the whole frame. I'm going to cheat by taking a scanline that directly overlaps where the radar is located which is the 204th row. Below is a snapshot of a video frame with a yellow box indicating the general region of the scanline location.
Suppose we take a horizontal scanline at the same location for each frame and stack them into a matrix where each successive frame is a new row in the matrix. The size of this matrix is (number of frames)x(scanwidth). Essentially this new matrix is a spatio-temporal image of one slice.
What's really interesting is the result from doing this procedure on a video with a periodic rigid object.
Below is spatio-temporal image scanline of the raw video sequence (no stabilization or differencing).
What's really interesting is the result from doing this procedure on a video with a periodic rigid object.
Below is spatio-temporal image scanline of the raw video sequence (no stabilization or differencing).
The radar in this video had one side that was blue and the other side was white. This is seen in the blue boxed region. Notice the shape induced by the periodic motion of the radar is distinct, like a helix. This video sequence did not have stabilization therefore the radar "vanishes" starting near the 70th frame. This is from the ship moving up or down in the image. It is also evident the video is not stabilized from the skewed vertical lines.
The next spatial-temporal scanline image is from a stabilized version of the video. The radar "helix" signature doesn't vanish. The period can be measured by counting the number of frames between each junction. Since this video was stabilized, the vertical lines are almost perfectly verticle. One can see the left 1/3rd of the radar armature is occluded.
These images took advantage of the fact that the radar has a distinct color difference between the front and back of the armature. Looking at difference frames can possibly remove the dependence on color and still afford the same "helix" shape.
The above image is the same spatial-temporal image using the stabilized difference video shown at the very beginning of this video. The colormap "Jet" was used to enhance the lines more. It's more useful to view this with a threshold as shown below. Again, the helix shape appears.
I will investigate other video sequences with this same approach and will see if the same results occur.
Another observation was made during this process. Pertaining the previous post, it seams that if I know the periodic rate of the radar, I may be able to stabilize the image. Whereas if I stabilize the image, a periodic signal will arise.
The images generated in this post is dependent on knowing where the radar is. A brute force method may be to move the scanline sequentially until this "helix" shape appears. Or, maybe do an angled scanline instead. The line would run from top-left down to the bottom-right corner (or vice-versa). Also, I could scan vertically which would provide results similar to the previous post.
The trick is coming up with methods that are accurate but not processor intensive.
Another observation was made during this process. Pertaining the previous post, it seams that if I know the periodic rate of the radar, I may be able to stabilize the image. Whereas if I stabilize the image, a periodic signal will arise.
The images generated in this post is dependent on knowing where the radar is. A brute force method may be to move the scanline sequentially until this "helix" shape appears. Or, maybe do an angled scanline instead. The line would run from top-left down to the bottom-right corner (or vice-versa). Also, I could scan vertically which would provide results similar to the previous post.
The trick is coming up with methods that are accurate but not processor intensive.
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