Overview
Overview
Key Features
Key Features
- Features Overview
- Person Detection
- Movement Detection
- Facial Properties and Recognition
- Configuration
- Image and Video
Feature Demos
Feature Demos
- Starting Feature Demos
- Person Detection
- Movement Detection
- Facial Properties and Recognition
- Image and Video
- Practice Use Cases
Developer Guide
Developer Guide
- Architecture
- Data Model
- Microservice Descriptions
- Microservice Configuration
- Tools
- Extending UVAP
Operation Guide
Operation Guide
- UVAP Operation Guide
- Microservice Operation
Introduction
Introduction
Ultinous Video Analytics Platform (UVAP) is a set of software services that can be composed and extended in a flexible way to build scalable, distributed video processing applications in multiple domains such as retail, security or elderly care.
Video Analysis Capabilities
UVAP provides a rich set of deep-learning-based advanced video analysis models. These models are industry leading in accuracy along with efficient processing. The available core models are the following:
- Head/face detection
- 3D head pose
- Face recognition, re-identification
- Full body re-identification
- Age
- Gender
- Human body pose
- Tracking
- Pass detection, counting
Example Use Cases
UVAP has been used to solve real world problems in different domains. For example:
Queue management system for retail stores. Customers are counted at the doors and in the queuing area in real-time. A prediction model can tell if there will be a queue in the next few minutes and staff is alerted before it happens.
Fall detection in elderly care homes. The system uses two 3D-calibrated cameras per room and runs human body pose estimation. Based on the body pose, fallen body pose can be recognized. An alert is sent to the staff in real-time. Staff personals can look at the alert, validate the images and decide to take action.
Measure waiting time on airports. Face recognition is applied at the entrance and exit points in real-time to measure actual waiting time. Prediction is applied and displayed to customers entering the queue.
Recognize unsafe escalator usage. Based on head detection, tracking and full body pose, different unsafe situations are recognized. Such as: wrong direction, leaning out, crowd