Jace & Glimpse
Jace
Hey Glimpse, I just got my hands on this new tiny camera that can record and compress video in real time. It’s super low‑power and has an AI that can detect faces and objects on the fly. Think of all the patterns you could pull from that. How do you usually analyze the data you collect?
Glimpse
I’d start by hashing each frame with its timestamp and coordinates, then run a Bayesian filter to update the likelihood of each face or object’s identity. The field manual, section 4.7, covers the low‑latency face‑tracking routine—keep a two‑second buffer, discard what doesn’t change the posterior, and you’ve got a clean data set. The rest is just noise.
Jace
That makes sense, and the two‑second buffer trick is clever for pruning. I’ve been tinkering with Bayesian filters myself, too. If the noise gets too high, a lightweight CNN might help refine the posteriors. How do you deal with false positives when the lighting changes?
Glimpse
I log every lighting change and its corresponding detection confidence. Then I adjust the Bayesian prior for that environment; if the confidence drops below the threshold in the manual, I discard the event. The CNN is only called on the residuals, and its output is fed back into the filter as a correction factor. That way false positives get flagged and the system learns to ignore the glare.
Jace
Nice, the Bayesian tweaking is solid. I wonder if a quick sketch of the confidence curve would help spot patterns.
Glimpse
Sure, sketch a quick line: x‑axis is time, y‑axis is confidence. Look for sudden dips—those are likely lighting shifts. The pattern of dips can tell you if the change is a regular event or a one‑off glitch. Then you can tweak the threshold accordingly.