ANONYMIZED CASE STUDY
Maritime Object Detection and Track-ID Delivery
An anonymized image and tracking workflow for vessel-focused computer-vision data.
Project context
A maritime dataset required object detection labels and separate tracking outputs across multiple subsets. Client identity and source imagery remain confidential.
Annotation challenge
The work required consistent object classes and geometry while preserving valid identities across tracking subsets and checking that the final export matched the required structure.
Data and task
- Maritime images and related tracking subsets
- Vessels and other guideline-defined objects
- Bounding-box and Track-ID outputs
Workflow
- Reviewed the project taxonomy and examples
- Applied guideline-based bounding boxes
- Maintained tracking identities within the required subsets
- Separated detection and tracking deliverables according to the requested schema
Important edge cases
- Crowded or partially visible vessels
- Small and distant targets
- Occlusion and uncertain object boundaries
- Track identity and sequence consistency
Quality assurance
- Missed-object and class review
- Bounding-box geometry checks
- Track-ID consistency review
- File, subset and export validation
Delivery outputs
- Client-required bounding-box JSON
- Separate Track-ID JSON deliverables
- Reviewed corrections and final structured delivery
Outcome
The annotated and reviewed outputs were organized by the required subsets and delivered in the client-requested JSON structures.
This case study is intentionally anonymized. No client names, confidential source images or unsupported performance claims are included.