Bounding box annotation for object detection in security helps AI systems identify and track objects such as people, vehicles, or suspicious items within surveillance footage. By drawing precise rectangular boxes around objects, security systems can enhance threat detection, automate monitoring, and improve response times.
Segmentation for thief detection enhances security systems by accurately identifying and isolating individuals engaged in suspicious activities. By classifying each pixel in an image or video feed, AI can distinguish a thief’s movements from the surrounding environment, improving accuracy in real-time surveillance and reducing false alarms.
Unitlab supports long-form video, synchronized camera views, images, audio, text, documents, and related event metadata.
Yes. Frame-accurate tracking, keyframes, interpolation, segmentation, temporal labels, and object properties support video intelligence datasets.
Yes. Teams can navigate long timelines, label temporal ranges and events, review key moments, and preserve annotation history.
Related camera streams can be grouped with shared temporal context for consistent multiview annotation and review.
Ontologies, classifications, properties, relations, temporal ranges, and object tracks can encode events and context without flattening the data.
Teams can search, filter, deduplicate, balance, version, and route selected footage into annotation and QA.
Yes. Bring Your Own Model workflows support domain models for pre-labeling, followed by human validation and correction.
Reviewer roles, issues, rework, approvals, instructions, and full history help teams maintain consistent labels across cameras and sites.
Yes. On-premises deployment is available for organizations that need video, models, and workflows inside controlled infrastructure.
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