Please enter the access password to unlock the warehouse stock counting portal.
Select a role to enter the correct workspace. The Warehouse Manager captures or uploads stock images and videos, runs the AI count and submits the report. The Audit Manager independently reviews the evidence and provides the final approval.
Capture images or scanning videos from a phone, use CCTV footage, run the computer vision count, validate exceptions and submit the report for final audit.
Review submitted stock counts, inspect image or video evidence, compare quantities and issue the final approval, recount or correction decision.
This dual-role application demonstrates the proposed workflow for warehouse stock counting. A Warehouse Manager can scan a stack using a mobile video, upload photographs, use CCTV footage, or capture length, breadth and height views. The computer vision model detects and counts visible stock, highlights exceptions and submits the verified report to the Audit Manager for the final decision.
The guide moves through the business problem, capture methods, AI counting, warehouse validation, Audit Manager approval, analytics and technical architecture.
The same count report moves through two secured workspaces with the original evidence, model output, user actions and timestamps.
Count visible bags in large warehouse stacks even when rows are uneven, labels are unclear or some bags are partially hidden.
Use object-specific models and counting logic for cartons, drums and other repeatable warehouse stock units.
Support mobile scanning video, CCTV footage, still photographs and length × breadth × height image sets.
The operator records a controlled walk-by video or takes clear photographs covering the complete stock stack.
Suitable warehouse cameras can provide scheduled frames or footage for periodic, event-based or ad hoc counting.
For regular stacks, three-side images can estimate rows × columns × depth and cross-check the visible-face object count.
The AI checks image quality, detects individual jute bags, boxes or drums, tracks objects across video frames and calculates the count with confidence.
Only lower-confidence items caused by overlap, damaged bags, shadows or obstruction are shown for quick Warehouse Manager review.
The Audit Manager reviews the submitted evidence, quantity variance and activity trail before approving, returning or requesting a recount.
Use mobile video, photographs, CCTV frames or three-side dimensional images.
Tag the facility, section, bay, stack, commodity, lot and expected system quantity.
Run the correct model for jute bags, boxes or drums and remove duplicate video detections.
Review only uncertain or hidden objects and apply controlled adjustments.
Preserve evidence, model output and Warehouse Manager actions for independent review.
Track stock variance, warehouse completion, low-stock signals and audit status through dashboards.
Reduce repeated manual counting for large stacks and frequent inventory checks.
Separate warehouse submission from independent final audit.
Use phone videos, CCTV, photographs or dimensional image sets.
Extend from jute bags to boxes, drums and other warehouse stock.
Move into the Warehouse Manager workspace and demonstrate the complete count-to-audit journey.
Convert mobile videos, CCTV footage and warehouse photographs into commodity-level counts, validation queues and inventory insights for jute bags, boxes, drums and future stock categories.
Lot JB-2407 in Section A is three bags below the system quantity.
Two boxes are visually missing from Bay B-08 compared with the inventory record.
Section D has been verified with 72 drums and no quantity variance.
Count stock from a controlled phone scan or suitable warehouse-camera footage.
Cross-check regular stacks using rows, columns and depth from multiple images.
Organise results by facility, section, bay, stack, commodity and lot.
Start with jute bags and extend to boxes, drums and other stored stock.
Complete each stage to simulate an end-to-end warehouse count from evidence capture to final audit submission.
The workflow adapts the validation and approval steps based on the operational purpose.
Use a guided mobile capture for immediate deployment, or connect fixed cameras as the solution scales.
Warehouse context selects the correct counting model and keeps every result traceable to the section, bay, stack and lot.
The new visual count will be compared with the system quantity, previous verified count and lot-level threshold.
Use the supplied jute bag image and scanning video, select another commodity sample, or upload a local image.
Play the actual mobile scanning sequence to show how the operator configures the count, records the stack, completes the quality assessment and receives the AI-generated result.
Select warehouse, commodity and section before recording.
Follow the guided sweep and maintain consistent movement.
The workflow checks coverage and recording quality before analysis.
Review the final count, confidence and dimensional breakdown.
The demo simulates image-quality checks, commodity detection, instance separation, video de-duplication and confidence scoring.
Lower-confidence objects are reviewed by the Warehouse Manager before the report is handed to the Audit Manager.
One partially hidden jute bag and one folded or damaged bag were retained with lower confidence.
Inspect detections, resolve stock exceptions and preserve a complete evidence trail.
Mobile Scanning Video · Section A / Bay A-03 / Stack 02
Today, 10:42:18 AM428 units detected · 96.8% mean confidence
Today, 10:42:26 AM2 lower-confidence bags placed in the validation queue
Today, 10:43:02 AMAwaiting Warehouse Manager review completion
PendingOnce the Warehouse Manager completes validation, the stock report moves to the Audit Manager with image or video evidence, AI detections, quantity comparison and a complete activity log.
Submitted and awaiting independent review
Final audit completed
Action required by warehouse team
Illustrative turnaround time
Every Warehouse Manager submission arrives with the original stock evidence, AI detections, manual adjustments, confidence information, warehouse context and activity history.
Audit decisions remain separate from the Warehouse Manager submission, preserving role-based accountability.
Prioritise by variance, confidence, location and submission age. Open any report to inspect the full evidence package.
Open a submitted reportReview image, detections and warehouse actions.
Complete audit checksConfirm evidence and quantity logic.
Record final decisionApprove, return or request recount.
Validate the submitted quantity using the original capture, detection evidence, metadata and warehouse activity history.
The final approval posts the record into the approved inventory history. A recount or return decision sends the report back to the Warehouse Manager with your note.
Approved reports retain the original image, submitted count, decision note, user actions and timestamps for traceability.
Translate count activity into faster decisions on stock risk, variance, warehouse completion and audit workload.
Section A is the main jute bag variance driver, contributing 34% of current stock discrepancies.
Box SKU BX-2026 requires validation because the visual quantity is two units below the system record.
Count productivity is improving, with mobile video sessions becoming faster after guided operator training.
Plan count waves for jute bags, boxes and drums, monitor bay completion, route exceptions and maintain final approval evidence in one workflow.
Bays remaining74
Open exceptions31
Active count teams8
Estimated completion1.6 days
A-03 / Stack 02 verified at 428 jute bagsMobile scanning video · Lot JB-2407 · 2 minutes ago
−3 varianceB-08 box count sent for reviewTwo-unit mismatch detected · 6 minutes ago
ExceptionTeam 3 started Section D drum countCCTV-assisted workflow · 11 minutes ago
In progressSection B sign-off posted76 bays · 14,920 boxes · 24 minutes ago
CompleteUse front, side and depth views to estimate rows × columns × depth, then cross-check the result using visible-object detection.
428 bags detected on the visible face. Three depth layers produce an estimated 1,284–1,296 range after occlusion adjustment.
The side depth is partially covered. Confirm the third layer before final submission.
Detect visible bags and calculate the number of rows and columns on the primary stack face.
Estimate the depth layers and identify missing, irregular or partially covered stock.
Compare dimensional estimation with object detections and confidence before Warehouse Manager approval.
Mobile video, CCTV and dimensional stack counting for large warehouse piles.
Count cartons by visible face, pallet, row, column and depth.
Detect circular or cylindrical units across pallets, rows and warehouse bays.
Estimate stock through rows × columns × depth and validate with object detection.
A high-level architecture for mobile video, CCTV, dimensional image capture, computer vision inference, review, final audit and inventory integration.
Operators can use a mobile web application to record a guided scanning video or upload photographs. The same ingestion layer can accept CCTV footage and three-side dimensional image sets.
Vision inference runs close to warehouse cameras while sensitive images and operational records remain within the organisation's environment.
Perform image inference at the edge and synchronise approved count results, dashboards and integrations through central services.
Centralise model hosting, storage, workflow and analytics in an approved cloud environment with secure camera or mobile ingestion.
A phased pilot establishes the manual-count baseline, validates jute bag counting with representative warehouse data and defines the production expansion path for boxes and drums.
Confirm locations, stack patterns, count process, camera options, inventory records and pilot success measures.
Capture representative images and videos across lighting, damaged bags, stack density, perspective and obstruction conditions.
Deploy the guided count workflow, review queue, dashboard and pilot integrations for controlled operational testing.
Measure performance by condition, close pilot gaps and define production architecture, rollout and model expansion.
Measured by commodity, stack density, camera angle and warehouse condition against verified ground truth.
Performance across shadows, damaged bags, uneven stacking, partial obstruction and changing warehouse lighting.
Time from capture through review and posting compared with the current process.
Percentage of images and objects requiring recapture or human confirmation.
Ability to identify meaningful quantity and location mismatches for action.
Decision point after pilot
Proceed to production only after agreed performance and operational measures are met on representative warehouse data.