Security data annotation presents a unique set of challenges and opportunities. By careful manual annotation of your visual data we will create a dataset that will teach your camera to recognize individuals, objects, animals, detect a movement, track an object through multiple video frames and even multiple security cameras.
Types of annotation that we provide:
- Bounding boxes
- Semantic segmentation (Polygon)
- Instance segmentation
- Skeletal (Skeleton) annotation
- Object tagging
- Frame-by-frame object tracking
Our annotation can be used in multiple security-related applications:
- Image annotation for recognition of weapons or dangerous objects
- Image annotation for face recognition
- Object or person tagging and tracking through multiple frames
- Object classification on security monitors
Image and video annotation for the recognition of weapons or dangerous objects
Analyzing an enormous amount of visual data from CCTV cameras and identifying dangerous objects in real-time could be exhausting (if not impossible) for human operators. That is where AI steps in: automated image recognition algorithms that alert operators of anything requiring attention.
We can annotate any type of security imaging data that will teach your system to react to potentially dangerous situation.
Image and video face recognition
We offer powerful tools and techniques to label faces under a variety of conditions. This includes advanced face matching scenarios, as well as assigning custom data structures to customer supplied visuals.
Object or person tagging and tracking
A carefully designed mix of customer and inhouse-generated data will teach AI to tackle most complex tag-and-track assignments, whether these are people, animals or objects traversing multiple fields of view.
Object classification on security monitors
AI can effectively analyze continuous streams of visual data to pre-screen and highlight any areas of concern.
We specify, generate and validate custom datasets that fine-tune pattern matching capabilities. This allows AI to assume 90% of visual classification and detection workload, separating human operators from raw image data, reduce staff stress and alleviate privacy concerns.