Count something real with a camera

Level 3: a month or more, real users. Use with: train-a-model, simple-dashboard. Resume line when done: Built {project_name}, a camera-based counter validated against hand counts, with a live daily dashboard — at {url}

Brief

Point a camera you own at something countable in your world (birds at the feeder, cars in your driveway, bees at the balcony planter) and build a system that counts visits automatically. Start with a motion-based counter as the baseline, then train a small detector on frames you labeled yourself. Ground truth is you: hand-counted hours of footage that the machine's numbers get checked against. The demo is a live dashboard of daily counts with the validation table and the detector's misses on display.

Personalize

• Your subject and your camera. An old phone, a webcam, or a trail cam, aimed at your own space. Frame the shot so it cannot capture neighbors, passersby, or anyone's windows; if a person could wander into frame, choose a different angle.
• Your count definition. What is one visit? A bird landing counts, a fly-past does not; write the rule before labeling, because every downstream number depends on it.
• Your ground truth. You, watching sampled hours of footage and tallying by hand with the written rule. Machine counts mean nothing until they are compared to this.

Milestones

1. Rig the camera and record a pilot day. Check framing, lighting changes, and that nothing private is in frame at any hour.
2. Write the counting rule and hand-count three sampled hours from the pilot footage. This is the first ground-truth set.
3. Build the baseline: motion detection with a threshold, tuned on pilot footage. Score its counts against your hand counts and record where it fails (wind, shadows, clouds).
4. Extract frames and label a few hundred with boxes or presence/absence, following the counting rule. Log the frames you were unsure about.
5. Split by day: whole days go to test, frozen. Same-day frames are near-duplicates and would leak.
6. Train a small detector (fine-tuned on your labeled frames) and turn detections into visit counts using your rule.
7. Hand-count three new hours from test days you have not watched before. Score baseline and detector against them, hour by hour.
8. Read the failures both ways: visits missed, and phantom counts. Collect example clips of each.
9. Wire up the pipeline to run on each day's footage and write daily counts to a data file.
10. Build the dashboard: daily counts over time, the validation table (detector vs baseline vs hand counts), and the miss gallery.
11. Run it for two weeks unattended, then write up drift: weather, changing light, anything that degraded counts after training.
12. Publish the limitations: one camera, one spot, and counts are estimates with the measured error, never exact.

Done means

• Counting rule written before labeling, and ground truth collected by hand against it, covering at least six hours across pilot and test days
• Motion baseline built first and scored; detector reported next to it against the same hand counts
• Test days never seen in training, with the day-level split explained
• Dashboard live with daily counts, the validation table, and real failure clips shown
• Two weeks of unattended operation, with drift honestly reported
• Framing documented as privacy-safe: your space only, no people captured
• Counts everywhere labeled as estimates with their measured error