How Gyrus AI Helped a Top European Automotive OEM to cut Video Anonymization Time by 90%.

One of the leading auto-makers of Europe which is at the forefront of developing autonomous vehicle technologies recently had a major challenge with their data processing architecture. As they were on a mission to bring autonomous cars to market, they required a large amount of real-world video data to train the machine-learning models effectively.

Industry Challenge:

Many automotive companies with the vision of achieving self-driven cars often struggle with the time-consuming and labor-intensive process of anonymizing training data. This task is very important when handling personal information, particularly in areas requiring conformity to the GDPR and other data protection laws, including the need to anonymize PII (Personally Identifiable Information) to comply with GDPR. When it is done manually, it becomes tiresome and time-consuming when blurring the license plates of vehicles or faces of people, and it can be so incorrect with inconsistent outcomes.

Client's Specific Problem:

The client's team was manually blurring license plates and faces in their video datasets, a process that was:

1. Extremely time-consuming: Taking up to 1,000 hours per month

2. Prone to human error.

3. Inconsistent across different team members.

4. A bottleneck in their development pipeline: Delays extending project timelines by 30%

This manual process significantly slowed down their whole processes and also hampered the sharing of data to their global teams because of the GDPR regulations.

Our Solution:

We proposed our state-of-the-art Video Anonymization AI model, leveraging advanced AI and ML techniques. Key features of our solution include:

1. Automatic Detection and Blurring: Identifies and blurs license plates and faces with ~98% accuracy.

2. Scalability: Processes large volumes of video data quickly, reducing processing times by 85%.

3. Consistency: Ensures uniform anonymization across all processed videos, eliminating human inconsistencies with 100% uniformity.

4. GDPR Compliance: By automatically anonymizing sensitive data, the solution helps ensure compliance with GDPR and other privacy regulations.

5. Lightweight & Easy Integration: Easily integrates with existing systems, runs on lighter GPUs and CPUs, and can be deployed on cloud, on-premise, or edge devices.

Implementation:

Gyrus team worked closely with the client to deploy our AI model on their own servers, utilizing their GPUs within their premises.

1. Dataset Collection: The client provided their sample video datasets for training and enhancing the model.

2. Data Preparation: In the next stage, the customer data is cleaned, enriched, and made ready for data science. At this stage, the data quality is measured and all the data enhancement techniques are implemented.

3. Model Development: Gyrus customized the video anonymization AI model using transfer learning, starting with prebuilt models tailored to the client's specific needs.

4. Quality Assurance: The model underwent explainability checks, bias evaluations, and differential privacy enhancements to ensure high accuracy and GDPR compliance.

5. Deployment: The AI model was deployed on the client's in-house servers, leveraging their existing GPU infrastructure for efficient processing.

6. Continuous Improvement: The model was regularly updated with new data and features to maintain accuracy, consistency, and reproducibility over time.

This approach ensured the client's data was processed efficiently, securely, and in full compliance with GDPR and other privacy regulations.

The Impact:

After implementing our AI-powered video anonymization model, the client experienced significant improvements in their development process:

1. Time Efficiency: The automated process reduced the time required for video anonymization by over 90%, allowing faster iteration in model training.

2. Improved Data Quality: Consistent and accurate anonymization significantly improved the quality of training datasets.

3. Enhanced Collaboration: GDPR-compliant datasets could be easily shared among global teams, fostering better collaboration and faster development cycles.

4. Cost Reduction: Significant cutting down of the amount of manual labor required, reducing labor costs by 70%.

5. Scalability: Enabled the client to process and use 5x-10x more training data, leading to more credible autonomous driving models.

Conclusion:

Thus, by solving their major concerns and offering an effective, fully automated solution, we not only helped them meet the privacy regulations but also contributed to enhancing their operational efficiency and data quality upgrades, and significantly reducing the overall project cost.

This case study highlights the potential of AI-driven solutions in the automation of data processing channels particularly in industries that value data confidentiality.

For other businesses that may encounter the same challenges while looking for ways on how to protect client’s data, Gyrus provides them with a sound method of protecting data while at the same time benefiting from useful data.

Keep in touch with us to find out how we can assist your organization in getting past data protection challenges and reaching your objectives. Write to us at info@gyrus.ai for more information.