Security systems powered by computer vision-based AI models have the potential to stop security threats before they escalate. Cameras equipped with AI project management tools can act as vital early warning systems, supporting security staff and law enforcement.
Public spaces are increasingly being viewed as targets for violence. AI security systems could help to reassure a concerned public by providing a reliable blanket of security.
AI-powered cameras can recognise objects, like vehicles, that may be involved in criminal activity. And, now smart security systems can identify potentially suspicious or threatening behavior in real-time.
Image and video annotation for security AI
The development and improvement of real-time threat monitoring AI is supported by computer vision annotation tool. AI security systems need to be trained with image labeling and video annotation tool to recognize objects and people. By applying the information to images and video, powerful training datasets can be created.
Data annotation tools, like Keylabs, allow human annotators to locate and label important features in digital imagery. Machine learning algorithms use this information to develop a sophisticated understanding of the world around them. Accurate training data is essential for AI security systems to function effectively in any environment.
Image classification is the most simple form of data annotation. For security AI training, this might mean labeling an image of a briefcase lying on a table as a “briefcase”.
Bounding boxes are used to add additional detail to image annotation tool. Outlining things with boxes using annotation tools makes object detection possible. For example, in security AI training, data annotators use bounding boxes to locate important objects, like suitcases or weapons.
Segmentation adds even more detail to training data by dividing every image or video frame pixel into a defined class. There are two levels of segmentation that can be accomplished with annotation tools:
- Semantic segmentation: Annotation tools can be used to define the precise shape of things like cars or people. Annotators find each example of a targeted class, outline it and assign it a class label.
Anything not captured by this forms part of the background of the image. In this way every pixel is correctly linked to a predefined category.
- Instance segmentation: This method extends segmentation by recording each instance of an object. So in the case of an image featuring suitcases, each suitcase would be assigned it’s own label by an annotator using an outlining tool.
Important annotation techniques
Annotation platforms are used to create effective training datasets for AI security systems. The Keylabs platform features a comprehensive range of annotation techniques that can be used for any custom dataset creation project:
- Bounding boxes: This is the fastest method of object location. Using an annotation platform, annotators drag boxes around objects and then label them according to pre-agreed categories.
- Polygon annotation: This technique allows developers to capture more complex shapes and information in their training data. By linking together small lines it is possible to trace the pixel outline of an object in a digital image.
- Skeletal annotation: This technique is used to locate the position of human body parts in images. Annotators attach lines to arms and legs and join them at points of articulation, like elbows and knees. This can help security AI models to recognise and interpret human movements.
- Points annotation: This technique is used to locate key features, usually on the human face. Workers use annotation tools to pinpoint eyes, noses and mouths in digital images. This kind of technique is particularly useful for security applications that rely on facial recognition and emotion recognition.
- Lane annotation: Training dataset creators use this technique to define the shape of linear structures, like roads and pipelines. Annotators draw parallel lines to locate the boundaries of these structures.
The challenge of video annotation for security AI training data
Most security AI models are designed to monitor and analyze moving security camera footage. It can be hard for security personnel to keep track of multiple cameras at one time.
As a result they can end up missing security incidents or potential warning signs. So computer vision models for security need to be able to interpret movement. Therefore annotated video training data is particularly important for the success of security AI applications.
However, video annotation can be a challenge for any AI company. Even a short piece of training footage can feature thousands of individual frames. Each frame needs to be carefully labeled in order to produce useful AI training data.
As a result video annotation can be extremely time consuming and labor intensive. Therefore trying to manage a large video annotation operation can be an expensive distraction for security AI developers.
Keylabs can simplify video annotation
The Keylabs platform has been designed to accelerate and simplify video annotation. With Keylabs multiple annotators can work on a large piece of video at the same time.
These annotated video segments can be seamlessly recombined, without affecting quality or object tracking. This makes video annotation for security AI projects faster and more accurate.
Other platform advantages
Keylabs also has a range of other features and options that streamline annotation so and improve final dataset quality:
- Quick outline function: This feature automatically detects the shape of an object in a digital image and creates a polygon label.
- Interpolation features: Keylabs uses algorithms to track objects across multiple frames of video. This also helps to speed up video annotation.
- Seamless integration: Any pre-annotation code can be seamlessly incorporated by Keylabs.
- Project management features: Keylabs has detailed workforce analytics that help to distribute tasks to those best equipped to handle them.
- Efficient options: Keylabs has a number of other time saving features, like useful keyboard shortcuts.
Keylabs helps security AI developers
AI can help security personnel and keep us all safer. Keylabs helps create powerful training datasets for this important technology by streamlining video annotation.