Camera-based Counting & Classification

Problem

A conveyor runs at 1.2 m/s with 4 product sizes (XS/S/M/L) sometimes overlapping. Mechanical sensors (load cells, light gates) miscount when items touch. Need a non-contact solution.

Architecture

60 fps GigE Vision camera → horizontal-line ROI across the belt → YOLOv8 detect → ByteTrack object tracking (counts each item once at line crossing) → contour analysis for size classification → calibration grid pixel→mm → webhook to ERP for live stock.

Stack & rationale

• ByteTrack: great for counting because it keeps IDs through brief occlusions.
• Calibration grid: one-time setup, accurate pixel → mm for size classes.
• GigE Vision: stable 60 fps, no dropped frames vs USB.

Results

• Counting error: 0.3-0.5% (across 50,000 test items)
• Size classification accuracy: 0.98
• Throughput: 8 items/sec, no bottleneck
• Per-product setup time: ~30 min (swap grid + retrain SVM for sizes)

Lessons

A great tracker beats a great detector for counting. ByteTrack > SORT when items overlap. Calibration grids MUST be printed on matte paper — gloss breaks corner detection.

Security & Compliance

• Privacy mode: face recognition stores embeddings (512D vectors) rather than raw images — non-reversible.
• On-device processing: no images leave the device (edge inference).
• Retention policy: ALPR/CCTV snapshots auto-deleted after 90 days unless flagged.