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Ratings and Reviews 0 Ratings
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What is Numina?
Numina conducts a comprehensive analysis of activities at the curb level. By gathering data in an anonymous and aggregated manner, the system yields valuable insights into pedestrian and vehicular traffic volume, their movement patterns, and overall dynamics on city streets. This information plays a crucial role in evaluating traffic safety, environmental considerations, and operational performance within urban infrastructure. Moreover, Numina aspires to establish a data utility that converts streets into a resource for developers. With privacy as a fundamental principle, Numina's design incorporates onboard computing in its sensors to process data while eliminating any identifiable images, resulting in datasets that prioritize anonymity and security. It distinguishes itself as the only computer vision sensor system specifically designed for urban settings, constructed from the ground up to provide insights without sacrificing privacy. While many stakeholders strive to build more livable communities, reaching a consensus on the most effective strategies often proves challenging. A variety of approaches are available for improving neighborhoods, and we are committed to helping cities identify the most effective solutions in a timely and equitable manner through a data-driven approach. Ultimately, our goal is to equip urban planners with the essential information to cultivate dynamic and inclusive public spaces that cater to the needs of all residents. Through this commitment, we aim to foster communities that not only thrive but also embrace diversity and accessibility.
What is Amazon Rekognition?
Amazon Rekognition streamlines the process of incorporating image and video analysis into applications by leveraging robust, scalable deep learning technologies, which require no prior machine learning expertise from users. This advanced tool is capable of detecting a wide array of elements, including objects, people, text, scenes, and activities in both images and videos, as well as identifying inappropriate content. Additionally, it provides accurate facial analysis and search capabilities, making it suitable for various applications such as user authentication, crowd surveillance, and enhancing public safety measures.
Furthermore, the Amazon Rekognition Custom Labels feature empowers businesses to identify specific objects and scenes in images that align with their unique operational needs. For example, a company could design a model to recognize distinct machine parts on an assembly line or monitor plant health effectively. One of the standout features of Amazon Rekognition Custom Labels is its ability to manage the intricacies of model development, allowing users with no machine learning background to successfully implement this technology. This accessibility broadens the potential for diverse industries to leverage the advantages of image analysis while avoiding the steep learning curve typically linked to machine learning processes. As a result, organizations can innovate and optimize their operations with greater ease and efficiency.
Integrations Supported
AWS AI Services
AWS App Mesh
Amazon Augmented AI (A2I)
Amazon Web Services (AWS)
BotCore
Descope
Mathstral
Orange Logic OrangeDAM
Qrvey
Quickwork
Integrations Supported
AWS AI Services
AWS App Mesh
Amazon Augmented AI (A2I)
Amazon Web Services (AWS)
BotCore
Descope
Mathstral
Orange Logic OrangeDAM
Qrvey
Quickwork
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Numina
Date Founded
2014
Company Location
United States
Company Website
numina.co
Company Facts
Organization Name
Amazon
Date Founded
1994
Company Location
United States
Company Website
aws.amazon.com/rekognition/
Categories and Features
Computer Vision
Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration
People Counting
API
Anonymous Counting
Benchmarking
Car Counting
Conversion Tracking
Data Export
Events Statistics
Heatmaps
Mood/Age/Gender Recognition
Motion Detection
Reporting / Analytics
Retail Counting
Staff Exclusion
WiFi Tracking
Zone / Area Monitoring
Categories and Features
Computer Vision
Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration
Content Moderation
Artificial Intelligence
Audio Moderation
Brand Moderation
Comment Moderation
Customizable Filters
Image Moderation
Moderation by Humans
Reporting / Analytics
Social Media Moderation
User-Generated Content (UGC) Moderation
Video Moderation
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Emotion Recognition
Facial Emotions
Facial Expression Analysis
Machine Learning
Photo Emotions
Speech Emotions
Video Emotions
Written Text Emotions
OCR
Batch Processing
Convert to PDF
ID Scanning
Image Pre-processing
Indexing
Metadata Extraction
Multi-Language
Multiple Output Formats
Text Editor
Zone Selection Tool
People Counting
API
Anonymous Counting
Benchmarking
Car Counting
Conversion Tracking
Data Export
Events Statistics
Heatmaps
Mood/Age/Gender Recognition
Motion Detection
Reporting / Analytics
Retail Counting
Staff Exclusion
WiFi Tracking
Zone / Area Monitoring
Session Replay
Eye Tracking
Form Analytics
Heatmaps
Mouse Tracking
Optimization Tools
Session Recording
Surveys
User Experience Analysis
User Feedback
Visitor Segmentation
