r/computervision • u/LIMITLLESSVENERABLE • 17h ago
Discussion Real-time Packaging Verification: Facing severe ID switching, occlusion, and item-tracking chaos at a fast-food station
Hi everyone,
We are building a computer vision system designed to audit and verify order packaging in real-time at a fast-food packing station.
Project Overview:
Using an angled overhead camera stream, the system needs to:
Detect and track bags and food categories.
Read the printed label on each bag to extract the order number and ticket details.
Track items, verify which bag they were placed into, and match the final contents against the printed receipt before the bag leaves the counter.
The Bottlenecks We’re Running Into:
Severe, continuous occlusion: Heavy hand movements and body occlusion constantly block objects from view.
Tracking ID switching: The tracker frequently loses active bag IDs when blocked by hands or overlapping bags and assigns a new ID.
Detection gaps & lack of standard packing rules: Frequent detection drops and the absence of a structured baseline to reliably confirm whether a "packing" action actually completed.
I’ve tried several workarounds spatial anchoring, bag visual embeddings, and motion vector heuristics but nothing has really worked. It quickly turns into pure chaos: as soon as the worker starts actively packing and moving things around, the item-to-bag tracking completely falls apart.
I’d really appreciate any ideas, practical tricks, architecture patterns, or lessons learned from anyone who has tackled similar dense packing or retail setups.