Automating Security Screening with AI-Powered Video Annotation

Security systems today rely on large amounts of video data for accurate monitoring and analysis.

One company, working on video-based security screening, faced a significant challenge in managing and annotating their massive datasets.

This case highlights how our solutions streamlined their workflow and improved the quality of their data annotations.

About the Customer

Our customer is a software developer at a leading technology company that develops advanced security screening systems powered by AI and ML.

They are responsible for analyzing security footage to ensure that individuals passing through screening areas do not carry firearms or other prohibited items.

The video data includes multiple attributes such as walking status, reasons for flagged behavior, gender, and clothing.

Managing and annotating these attributes manually for each person in the video became an overwhelming task for their team.

Data Volume and Accuracy Challenge

The customer’s team was tasked with annotating large volumes of video footage that captured individuals’ behavior as they passed through security checkpoints.

The dataset included various features such as walking status (both hands occupied, swinging, etc.), reasons for flagging behaviors (e.g., hands up, handbag, multiple people), and classifications such as gender and clothing type.

The manual annotation process was both time-consuming and prone to inconsistencies, leading to a need for automation in labeling these attributes while maintaining a high level of accuracy, especially in critical cases like identifying firearms.

How Labellerr Stepped In

To address these challenges, we proposed a comprehensive solution that integrated directly with their existing infrastructure. We provided:

? Seamless Data Management: We offered a direct integration with Amazon S3, allowing for the easy upload and management of their extensive video datasets. This streamlined their data handling process, improving both time and memory efficiency.

? Automation of Video Annotation: Using our advanced LabelGPT technology, we automated the classification of the video footage. Our autolabeling feature allowed for precise categorization based on walking attributes, gender, and clothing, significantly reducing the manual workload.

? ML Model Integration: For object detection, we integrated the YOLO (You Only Look Once) model, which efficiently categorized the required attributes. This model was tailored to ensure accurate classification, particularly in identifying whether individuals were carrying firearms.

Results and Impact

The results were immediate and impactful:

? Significant Time Savings: The automation of annotation tasks reduced the customer’s manual labeling time by over 60%, allowing them to focus on other high-priority tasks.

? Improved Accuracy: By leveraging our LabelGPT and autolabeling features, the accuracy of annotations improved, particularly in critical cases involving the detection of firearms and other security concerns.

? Efficient Data Handling: With the integration of Amazon S3, the customer experienced seamless data handling, which improved their overall workflow efficiency.

Conclusion

Our adaptive solution enabled the customer to tackle the overwhelming task of video data annotation with ease and efficiency.

By automating key aspects of their workflow and integrating advanced ML models, we helped them improve accuracy and reduce manual effort, ultimately contributing to enhanced security and screening performance.