Annotation for Security and Surveillance Industry

Our Data Annotation Process for Security and Surveillance Industry

1. Data Collection

Data is collected from various surveillance systems such as, CCTV footage, drone videos, body cams, and public spaces security cameras.

2. Data Preprocessing

Data collection is then followed by preprocessing steps such as filtering out raw data containing irrelevant footage (e.g., video frames without movement, or very low quality). Enables a better annotation from the information it has.

3. Data Annotation

The Pre-processed data then annotated using different annotation techniques

4. Quality Control

In addition to that, using automated tools or annotation experts check the annotation accuracy to make sure if the bounding boxes are set correctly or the labels are used consistently.

5. Iteration and Feedback

Machine learning models are trained using an annotated dataset. The performance of these models is evaluated and tested.

The dataset and its annotations are constantly updated to cope with the new evolving security scenarios (such as the emergence of new data threats or variations in the environment).

Use Cases in Security and Surveillance

• Perimeter Intrusion Detection:Restricted Area’s can automatically be flagged if a potential threat is spotted.
• Crowd Management: The data is annotated which helps in managing huge amount of crowd and detecting the abnormal behaviours in the public places.
• Vehicle Tracking: Tracking and identifying vehicles according to annotated license plate and traffic patterns.
• Suspicious Object Detection: Finding out unattended bags, dangerous items or strange objects in surveillance feeds.

While the process isn’t new, it is a critical step for developing secure and robust AI powered systems that promote safety, security, and surveillance in any environment and industry.