Business problem
The request was not limited to drawing a fixed region in a camera view. The operating rule also had to determine whether a vehicle actually unloaded in the permitted area, whether the person taking goods held current authorization, and whether a person crossed the exit boundary while still carrying goods. Each decision needed a reviewable event record instead of an unsupported label.
Project objective
The project established an edge-vision architecture and reviewable event model for three operational risks. It separates observable facts from business judgments, treats uncertain identity as a valid state, and retains card, QR code, work-order, and manual-review paths where face matching is unsuitable.
Project workflow
The project combines camera-role separation, multi-object tracking, business-state inputs, event rules, and evidence retention. ROI and line crossing provide the foundation; unloading recognition, person-goods association, and authorization decisions use project-specific temporal logic and validation.
- 01
Calibrate zones and camera roles
Define the permitted unloading area, vehicle reference point, cargo observation area, pickup area, warning line, and formal exit line. Assign views for vehicle position, unloading action, carried-goods tracking, and identity capture.
- 02
Detect vehicle position and unloading activity
Track the vehicle and determine whether its agreed unloading reference point is inside the permitted area. Confirm actual cargo transfer rather than treating parking or door opening alone as unloading.
- 03
Resolve task-level authorization
Combine identity quality, employee or contractor status, work order, time window, area, and goods batch. Return authorized, identified but unauthorized, unregistered, identity unconfirmed, or temporarily exempt.
- 04
Associate people and goods
Maintain person and cargo tracks through pickup, carrying, placement, handover, and occlusion. Use state transitions and confidence gates instead of a single-frame possession judgment.
- 05
Apply three event rules
Issue E1 for confirmed unloading outside the designated area, E2 for unauthorized or unconfirmed pickup, and E3 when a person crosses the formal boundary while carrying goods. Warnings and formal alarms use different thresholds.
- 06
Package evidence and review paths
Store event time, camera, zone, track references, rule state, authorization source, confidence, image, and video clip. Low-quality or ambiguous events are routed to manual review rather than forced into a definitive identity conclusion.
Project deliverables
- Edge analytics application
- Jetson deployment package with model, configuration, service startup, recovery, and resource monitoring.
- Rules and integration
- Zone configuration plus E1/E2/E3 event fields for HTTP, MQTT, relay, NVR, WMS, MES, or access-control integration.
- Authorization controls
- Registration and revocation, role and task permissions, non-face alternatives, audit records, and data-retention settings.
- Engineering documents
- Requirement specification, data and annotation rules, interface guide, deployment guide, evaluation protocol, known-limit list, and evidence package.
Project records and completed work
The following items are documented work products from this project. Performance metrics that are not supported by verifiable test or acceptance records are not inferred.
Requirement model
Three independent event definitions: E1, E2, and E3.Vehicle unloading, pickup authorization, and carried-goods exit were separated so each can be sampled, labeled, measured, and reviewed independently.
Architecture review
Two to four camera roles and one Jetson edge-processing path defined.The design documents where ROI, tracking, authorization inputs, custom temporal logic, evidence storage, and external systems interact.
Customer communication
A 14-page editable presentation was generated and rendered.The deck covers business rules, alarm flow, deployment, evidence, acceptance, schedule, and budget boundaries; page overflow checks passed.
Acceptance boundary
Precision, recall, latency, concurrency, and hardware model remain unverified.No site video, camera parameters, or controlled event dataset had been supplied when the solution was reviewed, so performance was intentionally left for a data-backed PoC.
Validation defined for the PoC
- Freeze camera placement, vehicle types, unloading direction, cargo classes, authorization sources, and event definitions before collecting metrics.
- Report E1, E2, and E3 precision and recall separately by camera, light level, occlusion, vehicle type, handling method, and identity quality.
- Measure P50 and P95 event latency on the selected Jetson configuration and record decoding load, inference load, memory use, temperature, and recovery behavior.
- Count identity-unconfirmed and state-uncertain events explicitly; do not remove them from the denominator or convert them into unverified unauthorized-person claims.
Case publication notes
This case comes from a real customer project and is published anonymously to protect confidentiality. The project scope, event definitions, technical architecture, acceptance framework, and deliverables reflect actual project records. Customer identity, site footage, internal systems, and private data are not disclosed. Accuracy, recall, latency, concurrency, final hardware selection, and production acceptance are stated only when supported by verifiable test or acceptance evidence; this page does not add unsupported figures.
Frequently asked questions
Short answers to the main implementation and acceptance questions raised by this type of project.
- Can a standard ROI rule determine whether unloading really occurred?
- No. ROI and line crossing can establish location, but unloading requires evidence of cargo transfer or a trusted business-system state. Parking or opening a door alone is insufficient.
- Must face recognition be the only authorization method?
- No. The design retains cards, QR codes, work orders, and manual review. Identity-unconfirmed is a valid state when face quality is inadequate.
- Can one camera cover every event?
- Not reliably in most loading areas. Vehicle position, cargo transfer, exit crossing, and identity capture often require different angles; the proposed single-point design uses two to four role-specific views.
- What must be measured before production rollout?
- Each event needs a frozen definition, representative positive and negative samples, precision, recall, latency, uncertainty rate, Jetson resource use, recovery tests, and a documented human-review process.