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What is Retail Sensing?

Testing reveals that video-based systems for counting individuals can achieve accuracy levels that surpass 98%. This impressive precision is obtained without infringing on individual anonymity. An overhead surveillance camera, whether CCTV or IP, oversees the movement of people within a specific area. This camera connects to a counting mechanism that accurately detects and records the number of individuals traversing a designated counting zone. The information gathered by these counters can be sent through several methods such as Wi-Fi, the Internet, IoT protocols, IP, Ethernet, RS485, or RS232. By combining these counts with real-time sales data from a point-of-sale (POS) system, businesses can gain immediate insights into their sales conversion performance. To effectively manage large entrances and spacious areas, multiple cameras can be linked across the ceiling space. Furthermore, an integrated video server allows for the simultaneous viewing of live footage alongside the respective people counts, facilitating accurate verification and remote system configuration. Once the system is established, it can be fine-tuned to transmit only the counting data over the network, which aids in conserving bandwidth. For remote commissioning purposes, it is also feasible to replay the videos utilized for counting. This comprehensive approach not only boosts data precision but also significantly enhances operational efficiency, leading to better decision-making for businesses. Ultimately, the integration of these technologies paves the way for smarter resource management and improved customer experiences.

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.

Media

Media

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Augmented AI (A2I)
Amazon Web Services (AWS)
BotCore
Descope
Orange Logic OrangeDAM
Qrvey
Quickwork
Trendzact
Unremot
Visionati
n8n

Integrations Supported

AWS AI Services
AWS App Mesh
Amazon Augmented AI (A2I)
Amazon Web Services (AWS)
BotCore
Descope
Orange Logic OrangeDAM
Qrvey
Quickwork
Trendzact
Unremot
Visionati
n8n

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

Retail Sensing

Date Founded

2013

Company Location

United Kingdom

Company Website

www.retailsensing.com/about-retail-sensing.html

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/rekognition/

Categories and Features

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

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