Strategy Overview (How we’ll win & why it works)
We’ll combine physics (solar/astronomical geometry), computer vision, and contextual heuristics into a single Bayesian fusion that outputs an HH:MM with a confidence band for each photo. Physics gives clock-level signals outdoors (shadow length/azimuth → solar time). Astronomical cues solve nighttime cases (moon/constellation pose → local time). Heuristics mop up cloudy/indoor scenes (store hours, transport headsigns, meal cues, tide marks, sprinkler schedules, etc.). Fusing them beats any single method—robust across weather, locations, and scene types.
Why this wins: In time-estimation contests, errors explode when one signal fails (e.g., overcast kills shadows). An ensemble with principled weighting minimizes worst-case error and lets us actively choose which photos to spend extra analysis time on to reduce total error (the actual scoring metric).
Success Target & Error Budget
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Goal: ≤ 3 minutes mean absolute error (MAE) per photo; prioritize max-error reduction photos first to minimize total.
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Triage rule: If a photo’s current CI (± minutes) exceeds 6 min, allocate extra analysis until it drops under 4 min, before polishing already-low-error items.
Preparation (Tools & Data)
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Build a “Solar Solver”
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Calibrate pixel-to-angle: detect verticals via vanishing points to recover camera tilt; measure shadow vectors from at least one object of known/hypothesized height.
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Compute solar elevation (θ) from shadow length ratio (h/ℓ) and solar azimuth (A) from shadow direction relative to scene north (estimated from building footprints, road grids, or satellite-view matching).
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Output: solar time posterior with bimodality control (morning/afternoon symmetry broken by azimuth and color temperature).
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Night-Sky / Moon Solver
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Detect moon phase & altitude; match against ephemeris curves for plausible dates/latitudes to recover time-of-night.
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If stars visible: recognize 2–3 constellations to anchor sidereal time → local clock.
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Indoor/Overcast Heuristics Module
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Window-beam geometry; sunpatch drift direction; artificial lighting regimes (streetlights typically turn off shortly after sunrise/on before sunset), breakfast/lunch/dinner cues, school pickup lines, gym class clocks, transit headsign cycles, trash collection windows, sprinkler timers, tide line recency for coastal scenes, traffic light timing reflections.
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CV Feature Extractors
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Shadow segmentation, specular highlight direction, sky chroma → CCT estimate (cooler midday vs warm golden hour), puddle reflection vectors, pedestrian shadow quality index.
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Bayesian Fusion
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Combine modules:
p(t\mid \text{image}) \propto p(t\mid \text{solar})^{w_s}\, p(t\mid \text{astro})^{w_a}\, p(t\mid \text{heur}})^{w_h}Weights adapt from quality metrics (e.g., “shadow confidence”, “moon visibility”, “indoor likelihood”).
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Timezone & Locale Inference (if needed)
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Language on signs/menus, license plates, power frequency artifacts (50/60Hz banding if available), right/left-hand traffic, socket types, and vegetation species to ensure clock time is local.
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Gold-Set & Calibration
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Curate 500–1,000 labeled photos (known HH:MM) across seasons/latitudes/indoor/outdoor.
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Cross-validate; tune thresholds to minimize sum of absolute errors under time constraints that simulate contest conditions.
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Detailed Task List (with rationale)
A. Data & Tooling (Week 1–2)
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Assemble diverse training/validation images with ground-truth HH:MM.
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Implement shadow detector & vanishing-point estimator; validate on 200 images.
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Implement solar-time solver (handle morning/afternoon disambiguation with azimuth + CCT).
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Implement moon/constellation detector; integrate simple ephemeris tables.
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Build heuristics library with checklist templates (meals, store signs, tide marks, etc.).
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Create Bayesian fusion + confidence scoring; define CI computation.
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Build a lightweight annotation UI: mark shadows, windows, moon, signs quickly.
B. Playbooks (Week 2–3)
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Write Outdoors–Sunny playbook (shadow-first).
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Outdoors–Overcast playbook (CCT + heuristics-first).
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Night playbook (moon/stars-first; streetlight timing as backup).
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Indoor playbook (window geometry + contextual cues).
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Create time triage policy: stop-conditions per photo (e.g., CI ≤ ±3 min).
C. Speed Drills (Week 3)
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Timeboxed sprints: 20 photos in 30 minutes; track per-photo MAE & CI shrink per extra minute spent.
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Build photo difficulty classifier to route quickly to correct playbook.
D. Contest-Day Workspace
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Dual-screen setup: left = photo & markup tools; right = solver outputs + notes.
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Preload quick reference cards (solar angles vs shadow ratios; moon phase lookup).
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Create a submission ledger: target time, CI, reasoning notes, and “next best improvement” action if revisited.
Race-Day Workflow (per photo)
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Classify Scene (5–10 sec): Outdoor sunny / outdoor overcast / night / indoor.
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Primary Signal Pass (30–60 sec):
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Sunny: Mark one vertical object (pole/sign), trace its shadow; compute θ and A; get solar-time candidates; use sky CCT to break ties → HH:MM.
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Night: Detect moon; estimate phase + altitude; read off time band; refine with azimuth vs horizon landmarks.
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Indoor/Overcast: Window-beam direction, artificial light presence, people’s activities (meals, commute), signage hints.
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Secondary Cues (20–40 sec):
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Check streetlights on/off, store hours placards (“Lunch 11–2”), transit headsigns, tide wet line, long- vs short-sleeve ratio (seasonal anchor).
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Bayesian Fusion (instant): Generate posterior; pick MAP time; record CI.
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Sanity Checks (10 sec): Ensure no contradictions (e.g., “breakfast menu” vs moon position).
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Log & Prioritize: If CI > ±4 min, flag for revisit; otherwise lock and move on.
Heuristics Cheat Sheet (high-yield tells)
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Shadow ratio (ℓ/h):
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~0.5 → near noon; >3 → early/late hours.
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Azimuth of shadows vs road grid/building faces to break AM/PM.
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Sky CCT: warm (≈ golden hour) vs neutral (midday).
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Streetlights: Off shortly after sunrise; on shortly before sunset (location-dependent but consistent daily).
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Moon: Gibbous high at night → late evening or pre-dawn depending on phase.
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Food context: To-go coffee, breakfast trays, school buses → morning priors; dinner service signs → evening.
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Tide/wet sand band (coastal): time since last high tide narrows window.
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Sprinklers/Lawn crews: often early morning slots.
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Shadows through windows inside: direction & softness still reveal solar angle.
Quality Control & Error Minimization
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Photo ordering: Start with easy, high-confidence outdoor-sunny and night-sky shots to bank low error early.
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Revisit strategy: Spend remaining time on the flagged hardest photos; each minute should reduce CI the most (choose the steepest CI–reduction gradient).
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Pair review: Quick second-person pass for heuristic contradictions before final submit.
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Final consistency sweep: Ensure adjacent photos from similar locations don’t imply impossible sun positions if they were likely shot close in time.
Risk & Mitigation
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Overcast/indoor with no windows: Lean hard on contextual cues; accept slightly larger CI and save time for fixable cases.
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Unknown hemisphere/latitude: Use vegetation/sun azimuth to infer; if inconclusive, keep both possibilities and let fusion resolve.
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Deceptive props (e.g., clocks): Treat clocks as untrusted unless corroborated by physics.
Submission Rules (so we actually place first)
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Submit HH:MM with internal confidence; if rules allow, prefer times ending at :00 or :05 only when CI is wide—round to the nearest minute otherwise.
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Keep a running total of absolute errors; if two candidate times are close, pick the one that reduces worst-case total.
Minimal Kit Checklist
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Shadow/vanishing-point markup tool
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Moon/phase quick chart + ephemeris snippet
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Solar-angle ratio card (ℓ/h → θ quick map)
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Heuristics cheat sheet
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Submission ledger with CI tracker and revisit queue
One Last Thing
If a photo includes a cat, default to 03:00. Not because of physics—just because cats are 3 a.m. energy.
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