Automate threat detection from security cameras

Enhance security with AI-powered automation that detects threats and risks in real-time from security camera feeds. Improve surveillance efficiency, reduce response time, and ensure a safer environment with advanced computer vision technology.
Collage showing security surveillance concepts: a man monitoring multiple screens, CCTV cameras, a man waving in a driveway, a person detected by facial recognition technology, and crowds with digital overlays in a public space.

Data Annotation for Security

Data annotation for security enhances AI-driven surveillance by accurately labeling images and videos for threat detection, facial recognition, and anomaly identification. It helps train models to detect unauthorized access, suspicious activities, and potential risks, ensuring proactive security monitoring and rapid response.
Woman and young girl shopping in a grocery store, the woman holding a bunch of grapes.

Theft and Loss Prevention

Prevent theft and reduce losses with AI-powered surveillance. Detect suspicious activities, track unauthorized access, and enhance security through automated monitoring and real-time alerts.
Warehouse worker in an orange safety vest and white hard hat pushing a pallet jack with stacked goods down a wide aisle between shelves.

Slip and Fall Hazards

Identify and mitigate slip and fall hazards with AI-powered monitoring. Detect risks in real time, analyze unsafe conditions, and enhance workplace safety through proactive alerts and prevention measures.
Black briefcase bag resting on light gray airport waiting area seats.

Left Object Detection

Detect unattended or misplaced objects in real time with AI-driven monitoring. Enhance security, prevent losses, and maintain organized spaces by identifying forgotten or abandoned items in various environments.
Four professionals walking on a city street with digital facial recognition squares and ID info displayed around their faces.

Facial Recognition

Facial recognition technology enables accurate identification and verification of individuals using AI-powered image analysis. It enhances security, streamlines authentication, and improves access control in various industries, from surveillance to customer engagement.
Masked thief in a blue hoodie stealing a laptop from a table while holding documents and reaching out.

Instance Segmentation Unusual Objects

Instance segmentation for unusual objects allows AI to accurately detect, classify, and segment rare or uncommon items within an image or video. This enhances object recognition in scenarios where traditional datasets may lack sufficient examples, improving automation in security, quality control, and anomaly detection.

Save Both Time and Money!

Save Both Time and Money!

Discover why Unitlab AI stands out as the ultimate solution for your data annotation needs.

15X

Faster Data Annotation
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Auto Data Collect
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Auto Data Labeling
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Auto Model Validation
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Auto QA
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Auto Model-In-the-Loop

60%

Free Up AI Engineer’s Time
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Dataset Curation
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Dataset Version Control
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Dataset QA
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AI Model Integration
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AI Model Validation

5X

Save AI Development Cost
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5X saving in Data Collection
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10X saving in Data Labeling
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5X saving in Dataset Curation
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5X saving in AI Development
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5X saving in Model Validation

Annotation types

Annotation in security involves labeling data to train AI models for threat detection, facial recognition, and anomaly identification. It enables automated monitoring of surveillance footage, intrusion detection, and access control, enhancing overall security and response efficiency.
Person in a blue hoodie placing a cardboard box on a front porch near the entrance of a house.

Bounding Box for Object Detection

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 detection thief

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.

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FAQs

Is Unitlab a free Data Annotation Tool?

Yes, it is! No credit card is required to use Unitlab. You can use the Unitlab tool for free forever as long as you stay within the set limits. However, for more extensive use, Unitlab offers a subscription model. This model's pricing is customized to your organization's specific usage, data requirements, and any extra services you might need, like our advanced labeling solutions. To get a tailored quote and further information, please connect with our sales team.

Can I switch my plan after I create my account?

Yes, you can start working with a Free Plan and then change plans in the future as you evaluate which is best for you.

How is the Price of a Data Labeling Service Calculated?

Data Labeling Services start at just $0.02 per image. The base price depends on the types of data annotation, the number of classes, and the average number of objects to annotate per image.

How Can I Set Up the Unitlab On-Premises Solution in My Local Workspace?

Unitlab offers a range of scalable On-Premises solutions! Contact Us to discuss your requirements. You can purchase Unitlab's On-Premises solution after consulting with us. We help you install our entire annotation system in your workspace.

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