CHALLENGE
The challenge
It was necessary to blur the license plates of vehicles appearing in videos shot at stores for privacy protection. There were a large number of cases, and manual work had its limits.
License plates in storefront videos,
Automatically mask in real-time with AI
Prime Market-listed automotive parts chain with nationwide operations
SYSTEM OVERVIEW
- 01Video ingestion
- 02AI detection
- 03Masking
- 04Verification and correction
This is a conceptual diagram organizing the development scope. It is not an actual system screen or architecture diagram.
OUR WORK
Our approach
Responsible for creating training data, training and tuning the license plate detection model. Built a production system that integrates video ingestion, detection, masking, and output into a single pipeline.
OUTCOME
Delivery & operations
Stable operation without manual work. Corrections can be made from the operation screen for areas missed by the model, achieving both accuracy and operability. Infrastructure monitoring and operations are continuously handled after release.
WHAT WE BUILT
Specific construction scope.
The tasks I was responsible for in this project
4 system areas.
Number plate detection model
From creating training data to model training and tuning.
Real-time automatic masking
Pipeline for production use that handles video ingestion, detection, blurring, and output.
Operator interface
Upload of video, confirmation of results, and manual correction of areas missed by the model.
Infrastructure & operations
From building the production environment to monitoring and operation, we continue to take responsibility after release.
RELATED SERVICESSimilar challenges,
Similar challenges,
Do you have similar challenges?
We will consider the implementation method according to your company's business, data, and existing system conditions.
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