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Finding Webinar Slides Without Following the Speaker's Camera

A 5% frame-change threshold missed seven synthetic slide transitions; revised region rules found the boundaries while exposing seven clipped webinar inserts.

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Technical reviewer: Victor Sineglazov, AI Scientific Advisor.

Our first slide detector missed seven of thirty known transitions in a synthetic lecture. Raising its tolerance for camera movement would not recover those transitions. The detector needed to distinguish small changes in slide text from movement elsewhere in the frame.

We were adding recorded lectures to a professional learning platform for aesthetic medicine. A slide needed a searchable representation and a time interval linking it back to the video. On October 12, 2026, we tested a simple image-difference approach before adding another model to the ingestion pipeline. The test exposed separate problems in transition detection, cropping and duplicate segments.

Build a video whose changes can be counted

The synthetic recording contained thirty slides across thirty-one showings. One slide appeared twice. Several slides built their bullet lists incrementally, another faded to black and a brief forward transition showed a slide for two seconds. A changing waveform occupied the lower-right area in place of a speaker's camera.

That waveform mattered. A detector that simply counted changed pixels could interpret the simulated speaker area as a continuous source of new slides. A detector that excluded everything moving could lose the text changes it was supposed to find.

We paired the synthetic recording with one real FDA webinar. The layouts differed: the webinar placed the slide beside a speaker camera and included full-screen title cards. The synthetic ground truth supplied known boundaries; the webinar supplied layout behavior that the generator did not reproduce.

Neither recording represented a library of camera angles or procedure videos. This was a bounded engineering test of slide-based lectures.

Different slide text can change very little of a thumbnail

The detector compared neighboring thumbnail images. At 160 by 90 pixels, lines of text become small gray bands. Replacing text inside the same slide template may change much less of the image than a viewer expects.

In the synthetic recording, adding a bullet changed up to 1.7% of the frame. Known slide transitions included changes around 2.7% and 3.0%. Quiet seconds stayed at or below 0.35%. A 5% threshold therefore removed meaningful transitions along with noise.

Selected results from the October 12 slide-detection spike
Configuration Synthetic boundaries found Remaining problem
Original 5% change threshold 23 of 30 Seven known transitions were missed.
2% change threshold 30 of 30 The crop could still include the changing waveform.
2% plus revised region rules 30 of 30 Brief forward-and-back moves still duplicated webinar segments.
Revised region rules plus adjacent-image merging 30 of 30 The shared crop still clipped seven full-screen webinar inserts.

The threshold change recovered the known boundaries. It did not establish that the extracted image showed the entire slide or that the segment sequence was useful for indexing.

Separate scene changes from evidence about the slide region

A single region-frequency setting could not find a suitable crop in both recordings. One setting left the webinar crop covering the entire frame. A stricter setting cut off the synthetic title slide and a slide with seven lines of text.

We changed how pixels contributed to the region estimate. Changes affecting more than half the stationary pixels were treated as scene changes and excluded from that calculation. A pixel also needed at least a quarter of its changes to coincide with slide transitions before it could contribute to the slide region.

Those rules addressed different evidence. A full-screen title card should not enlarge the crop used for ordinary slides. A waveform edge should not enter that crop merely because it sometimes changes when a slide changes.

With both adjustments, the synthetic crop retained the text and excluded the waveform. The webinar crop isolated its slide area. Treating the two adjustments as independent variables made the result easier to inspect than tuning a single percentage until one example looked right.

A brief visit to the next slide can duplicate the current one

The webinar speakers sometimes opened the next slide for one or two seconds and returned. The existing handling of these brief segments merged a glimpse with the following segment. That could leave two neighboring segments representing the same image.

We added a pass that merged adjacent segments when their images differed by no more than the selected change threshold. The webinar sequence fell from eighty-one segments to seventy-one. The synthetic sequence did not change under the selected setting.

That reduction was a count of segments produced from this recording. It was not a measured reduction in model cost or a general estimate of how often speakers revisit slides. Increasing the merge threshold too far also merged genuine synthetic slides, so fewer segments alone could not choose the setting.

A correct boundary does not guarantee a complete image

The common webinar crop clipped seven full-screen inserts, including title and speaker cards. The region that worked for ordinary slides could not describe those inserts completely. The detector had found a usable recurring region while retaining a documented exception.

The later live checks on October 13 and 14 recorded all thirty synthetic boundaries within one second of ground truth. The staging run reported a maximum difference of 380 milliseconds. The real webinar produced seventy-one boundaries matching the spike to the recorded second. These checks supported the implementation's handling of those files.

They did not prove the correctness of generated slide descriptions. They also did not make every slide retrievable: one synthetic slide remained below the retrieval cutoff, as described in our case about corpus-dependent retrieval thresholds.

For the next evaluation, we would keep boundary error, crop completeness and retrieval outcomes in separate records. The seven clipped inserts belong in the crop record even when every transition has a matching timestamp.

Evidence: the October 12 video spike, October 13 and 14 live checks and October 14 release note in our development records. The synthetic recording and one webinar define the sample. We have not rerun the detector for this publication or redistributed the source video.

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Dmytro Ihnatov is the Lead Backend Developer at Pharos Production. He documents backend architecture and research experiments from the team's work on a professional learning platform for aesthetic medicine.

Dmytro Nasyrov, Founder and CTO at Pharos Production
Dmytro Nasyrov Founder & CTO Let's work together!

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