Maximize Computer Vision
Security & Surveillance with Labellerr

Some of the production usecase example where Labellerr's revolutionary "Smart Feedback Loop" helping ML teams to get ground truth labels and model training super fast and simple.

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Maximize Computer Vision Security & Surveillance with Labellerr
Why us

Potential to enhance security & surveillance

Computer vision has the potential to enhance security & surveillance of different spaces. Businesses want to employ AI within their technology. Be it manufacturing plant or public places or private property computer vision based security systems gaining high traction.

OUr usecase

Production usecase examples

Safety compliance

Safety compliance

annotation in security and surveillance involves labeling data points to train machine learning models for tasks such as object detection, person identification, behavioral analysis, threat detection, and fraud detection to enhance security measures.
Face detection

Face detection

Image annotation in face detection enhances machine learning models to identify and locate faces accurately.
Vehicle Surveillance

Vehicle Surveillance

Image annotation services in-vehicle surveillance refine machine learning models to identify and track vehicles accurately.
Perimeter sensing

Perimeter sensing

Draw the boundary and mark the perimeter to secure a closed boundary, offices or facility
City management

City management

Waste management, theft detection, burglary detect, traffic management using computer vision
Anomaly detection

Anomaly detection

Image annotation in anomaly detection involves labeling data to train models to identify unusual patterns or outliers.

Labellerr: Expert Data Annotation for Security & Surveillance

Data Annotation for Security and surveillance

Data Annotation in Security and surveillance is the process of labeling data points to train machine learning models for various security tasks. This includes:
  • Person identification, ¬†which involves recognizing individuals based on facial features or gait.
  • Behavioral analysis identifies suspicious actions, such as loitering.
  • Threat detection pinpoints potential hazards, and fraud detection aims to identify fraudulent activities.

Datasets available for security & surveillance use cases

COCO Dataset

A large-scale object detection, segmentation, and captioning dataset

164000
Items
40
Classes
164000
Labels
COCO Dataset

Driver Drowsiness

Real-Life Drowsiness Dataset(RLDD)

41790
Items
2
Classes
41790
Labels
Driver Drowsiness

FAQ

What is data annotation, and how does it relate to security and surveillance?

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Data annotation involves adding labels to information to help computers understand and make sense of it.

AI and computer vision increases home security by analyzing images, detecting threats, and training systems for burglary and theft detection. It can be employed in various domains like Weapon Detection recognizes weapons, enhancing public safety, Facial Detection improves accuracy in security, etc.

Why is accurate data annotation crucial for security and surveillance systems?

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Accurate data annotation plays a critical role in optimizing the functionality and dependability of AI and computer vision models used in security and surveillance systems.

The precise labeling of data, including the identification of objects, individuals, and activities in images or videos, is vital for effectively training these models.

How can accurate image and video annotation improve security monitoring?

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Accurate annotation of images and videos improves security monitoring, allowing AI and computer vision models to recognize objects and interpret activities more precisely.

Can data annotation services be customized for specific surveillance needs?

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Yes. Data annotation services can be customized for specific needs.

Customizable data annotation services cater to specific surveillance needs, tailoring AI training for precise recognition and interpretation of objects and activities, thereby enhancing security and surveillance applications.

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