AI can keep businesses and public spaces safe. Computer vision models can take on the burden of security surveillance and ensure that camera systems are monitored 24/7. Developers in this sector are beginning to bring AI applications to market that can support security staff by giving advanced warning of potential threats.
Precise data annotation is the cornerstone of these exciting use cases. High quality annotated training data leads to better performing final models. However, it can be a challenge for AI companies to secure accurate image or video annotation and labeling. The right mix of annotation services can transform security AI projects.
Today’s blog will look at the need for precision in security AI. Then we will identify the applications currently being developed in this sector. Finally, we will show how annotation services can best support security AI projects.
Accuracy is vital for security AI systems
Security AI models are meant to keep us safe. If these autonomous systems make mistakes it can end up endangering people or letting crimes go undetected. This means that model performance is especially important for the continued success of AI in this sector. Security AI systems are also often required to identify individuals.
Incorrect identifications can have serious consequences and could lead to faulty legal proceedings and prosecutions. Accurately annotated training data can help safeguard against these potential issues. It is vital that the public consider security AI models to be trustworthy if they are to be adopted more widely.
AI models can improve safety by automating aspects of security monitoring. This allows security staff to focus on more important things whilst maintaining constant vigilance. The following applications are part of this new paradigm:
- Object and person video tracking: The ability to follow individuals and objects across multiple fields of view is vital for security. AI systems can handle multiple complex track-and-tag assignments simultaneously, giving security staff a vital edge.
- Facial recognition: Airports and secure facilities are increasingly turning to facial recognition to streamline security processes and stop potentially dangerous individuals. This technology can reliably identify people in digital images by analyzing the unique location of individual facial features.
- Emotion recognition: AI models can identify distinct emotional states by referencing facial expressions and body movements. Signs of distress, anger or agitation could be indications of security threats. By spotting these subtle indications AI can give security staff early warning of potential violence.
- Weapon and dangerous object recognition: It can be hard for human security staff to monitor thousands of hours of security footage. AI models can watch digital video feeds and identify weapons and dangerous objects. They can then trigger security alerts and allow human staff and law enforcement to assess the threat.
An effective package of annotation services
The applications detailed above rely on precisely annotated training images and video. Keymakr is an annotation provider that can provide the right mix of features and services to developers in the security industry:
- Annotation techniques for security AI training: Keymakr offers a full suite of annotation techniques for security AI training. This includes: key points for facial recognition and segmentation for dangerous object recognition.
- Video annotation: Labeling video frames is time consuming and labor intensive. Keymakr’s annotation tool is designed to streamline video annotation. Using the tool multiple annotators can work on the same piece of video simultaneously.
- Scalable services: Outsourcing annotation allows security AI teams to scale data labeling up and down as needs change.
Professional teams: Keymakr has an in-house team of professional, trained annotators. Managed annotation teams ensure a higher quality of training dataset.
Contact a team member to book your personalized demo today.