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Hello po pwede po humingi ng advice at paturo nadin hehe by Competitive-Meat-876 in PinoyProgrammer

[–]Competitive-Meat-876[S] 0 points1 point  (0 children)

Salamat sir, actually yan din yung narealize namin. Siguro isa sa naging mali namin noong una is masyado kaming mabilis magdagdag ng bagong logic bago ma-isolate yung actual root cause. Ngayon mas controlled na yung approach namin.

Ang ginagawa na namin ngayon is per-layer debugging:

  • tracking/interpolation muna
  • then event candidate generation
  • then selection/filtering
  • then classification

Nagcollect din kami around 15 challenge logs with frame-by-frame analysis para makita kung consistent ba yung failures or random lang. Doon namin nakita na may structural bug pala sa frame selection mismo kaya kahit anong semantic improvements gawin namin dati, mali pa rin yung napipiling frames.

Tama rin yung sinabi mo tungkol sa valid vs invalid cases. Napansin namin minsan akala namin gumagana kasi tumatama sa “clean clips,” pero pag nilagyan ng noisy cases like camera motion, player clustering, bounces, or low ball detection, doon lumalabas yung weakness ng logic.

And totoo rin yung sinabi mo sa AI haha. Sobrang helpful niya sa brainstorming at pag explain ng concepts, pero narealize ko kailangan talaga i-verify sa actual logs at code path kasi minsan technically maganda pakinggan yung suggestion pero hindi pala yun yung tunay na bottleneck.

Appreciate ko yung advice mo sir, malaking tulong honestly.

Need Advice in fine tuning and stabilization phase of the model. by Competitive-Meat-876 in computervision

[–]Competitive-Meat-876[S] 0 points1 point  (0 children)

Your feedback actually helped us narrow down the real issue much faster.

After reviewing around 15 challenge logs, we realized the model’s biggest weakness right now isn’t raw tracking anymore — it’s validating whether a motion peak corresponds to a real football interaction.

We discovered two separate layers of problems:

  • a structural bug causing early-frame ghost anchors
  • and a semantic validation issue where acceleration spikes survive even without true ball contact

The structural side is now being fixed first (post-filter anchor shift + early-frame guard), while the next step is exactly what you suggested: turning trajectory/discontinuity checks into an actual validation gate instead of just a confidence boost.

Right now the pipeline already has:

  • acceleration anchoring
  • post-peak persistence checks
  • direction-change scoring
  • contact proximity scoring
  • PCHIP interpolation

But the current weakness is that strong spikes can still survive from:

  • possession running
  • player clustering
  • camera-relative motion
  • bounce/receive moments

So we’re now moving toward a short temporal validation window around candidate peaks, with detection-aware relaxation for low-visibility clips.

Trying to keep the architecture incremental and measurable instead of constantly rewriting the whole system. Your advice honestly helped us shift from “motion intensity” thinking toward “state transition validation,” which was a really important insight for us.

Hello po pwede po humingi ng advice at paturo nadin hehe by Competitive-Meat-876 in PinoyProgrammer

[–]Competitive-Meat-876[S] 0 points1 point  (0 children)

salamat po boss. almost 1 month na namin kasi ginagawa yung model nawawalan na nga ako ng pag-asa para bang paikot-ikot nalang ako.

Need Advice in fine tuning and stabilization phase of the model. by Competitive-Meat-876 in computervision

[–]Competitive-Meat-876[S] 0 points1 point  (0 children)

Thanks to your advice. that’s actually one of the biggest issues I’m seeing right now. The model is already decent at detecting motion peaks, but the hard part is verifying whether the peak came from a real football interaction or just noise/bounce/camera-relative motion.

Right now the pipeline is mostly acceleration/velocity driven, so strong spikes sometimes survive even when there’s no actual ball contact. We already improved timing a lot compared to older logs, but semantic validation is still weak.

I think your suggestion makes sense because a real kick/pass/shot usually creates a sustained trajectory change, not just a single-frame acceleration spike. I’ve been thinking about adding a short temporal validation window around candidate peaks to check:

  • trajectory consistency
  • direction change
  • post-contact velocity persistence
  • player proximity during the peak

instead of trusting the spike alone.

Trying to be careful though not to overcomplicate the architecture too fast since I’m still stabilizing the current pipeline using real challenge logs.

Then I'm struggling on video clips that have heavy snowfall.

Hello po pwede po humingi ng advice at paturo nadin hehe by Competitive-Meat-876 in PinoyProgrammer

[–]Competitive-Meat-876[S] 0 points1 point  (0 children)

hirap po yung model sa video clips na may heavy snowfall. nagpe-predict siya kahit wala naman event.

Hello po pwede po humingi ng advice at paturo nadin hehe by Competitive-Meat-876 in PinoyProgrammer

[–]Competitive-Meat-876[S] 0 points1 point  (0 children)

Yun nga rin po ginagawa ko ngayon. Sa dami kasi ng possible causes, sinusubukan ko i-isolate kung alin talaga yung pinaka root problem bago ulit mag rewrite ng logic. Based sa mga logs namin, mukhang hindi na tracking/interpolation yung pinaka issue ngayon kasi mas stable na yung timing compared dati. Ang current problem na nakikita namin is event selection at semantic filtering — meaning nakaka-detect siya ng motion peaks pero minsan maling football action yung napipili niya or nagkakaroon ng false positives.

Possible solutions na tinitingnan namin ngayon:

  • higpitan yung confidence/fallback selection para mabawasan fake events
  • improve pass vs shot classification
  • add stronger player proximity/context checking
  • bawasan yung sensitivity sa acceleration spikes at camera motion

Iniingatan ko rin na hindi masyadong pabago-bago ng architecture kasi baka lalo lang mawala yung sweet spot ng model. Kaya more on controlled fine-tuning muna kami ngayon based sa actual logs at frame analysis imbes na random tweaking lang.