Distant Viewing TV: Initial Training Set

22 June 2017

We construct a training set of labeled scene and shot breaks from a single episode of Bewitched. Using this, we are able to verify the performance of our shot detector. It also allows us to sanity-check our face detector.

This post is part of a series about the Distant Viewing TV project. To see a full list of available posts in the series see Distant Viewing TV: Introduction.

At this point, we have a prototype for: face detection, face disambiguation, and shot detection. These were tested and tuned by randomly applying them over shot snippets of a single episode of Bewitch. The next logical step to me seemed to be coding each and every shot break in the episode. This way, we can be more certain that the shots are all being captured and feel confident pushing forward. I went through the episode frame by frame, coding where each shot took place, the scene location, and whether a shot depicts a close-up on a single character.

Here is the shot detector metric against my original coding over a range of frames near the end of the episode:

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For many of the breaks there is a clear and obvious signal in our detection algorithm. There seems to be only one real potential false signal around 31750; it occurs during a camera panning the set at a relatively fast rate. However, we can likely remove these anamolies by not only using a fixed cut-off but also looking to see if the metric ‘jumps’ at a specific spot. In this negative example the metric smoothly increases and decreases from a large value, unlike in any of the positive examples.

More worrying are the five shot cuts that do not seem to be picked up by the metric. Looking closer at them, these all refer to fade-ins and fade-outs. Given the nature of a fade, all happen slowly and do not make the metric spike. These fades need to be handled as a special case by detecting when the scene is entirely black. Detecting black scenes required a bit of work because the HSV color model has many equivalent ways of representing pure black. I then switched out saturation for chroma; this makes the color space look like an ice-cream cone rather than a cylinder. Once applied, I then coded each frame with whether it was completely black or not. Looking at all of the shot breaks, we can see that nearly every one is detected correctly with the exception of the cartoon intro between frames 3000 and 4000 (I did not include cartoon shot breaks in my data):

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The spikes in the last plot right at the end of the episode are title cards that probably should be considered shot breaks but were not included in my training data (though they are found by the fade-in fade-out detector). The only values that seems like a legitimately bad point is the false negative around frame number 4400 and the false positive around 28300. In the first example, Endora magically appears outside of Darrin’s office in a cloud of strobe lights. You can see the bad data point in all of its 1960s television special effects here:

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In this case I am truly stumped. I think for now treating this as a shot break is probably not too terrible. The false negative comes from a cross-fade between two scenes in the Stephens’ house. This seems possible to fix, but niche enough to avoid worrying about too much at the current moment.

Coding just the shots took around 4 hours, and I did not have time to code all of the characters in each shot. We can get some validation for the face detector though, keeping in mind that it is already known to be fairly noisy with regard to just a single shot. For each character, I identified each detected face with a character if the similarity score exceeded 0.7. Here are the scores for each character for each detected face. I’ve colored the points by the identity of the scene.

The secretary is only in the office scenes, and is in fact detected nowhere else:

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Endora is similarly only found in the kitchen and outside of Darrin’s office:

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Larry does not show up until the office scene, is the only character in his own house, and never enters the Stephens’ kitchen:

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Darrin is in the majority of scenes, but not in Larry’s house nor is he in the kitchen after the introduction. We see this show up in the dataset:

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Samantha is oddly missing from the first scene until Darrin returns home about halfway through the episode. The character detector makes one errant identification outside of Darrin’s office but otherwise seem to work well:

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The worst character is the character I just called ‘Lady’, the client Darrin is trying to win over in the episode. She is mistaken for Samantha in a few opening frames as well as towards the end of the episode:

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Overall these indicate that the classifier is working reasonably well.