VisionAI Apps for Workplace Safety. Pretrained & Ready to deploy.
VisionAI offers a collection of pre-trained apps tailored for workplace safety use cases. Developed by Visionify as part of the Workplace Safety suite, VisionAI is ready for production deployment and accessible through web-based GUI.
What is VisionAI?¶
- A Platform to run AI scenarios for CCTV cameras.
- Choose from the list of scenarios here for workplace safety & building security.
How does it work?¶
- Migrate CCTV feeds to the cloud. Manage cameras, choose AI scenarios and configuring alerting from cloud.
Key features of VisionAI include:
No hardware installation: Works with any IP/security cameras using RTSP streams. No need to install any new cameras, sensors, or other hardware.
User-friendly: Easy-to-use web interface for managing cameras and associated apps, catering to both technical and non-technical users.
Production-ready: Apps are trained on diverse, carefully curated datasets from industrial and academic sources, ensuring out-of-the-box functionality.
Customizable: Allows app customization and model fine-tuning with a flexible architecture based on the NVIDIA Triton server. Refer to customization documentation for more details.
Integrations: VisionAI currently integrates with Azure Event hubs, Redis PubSub for reports, alerts and notifications. We have roadmap plans to add support for other message brokers as well.
VisionAI offers a variety of workplace health and safety scenarios, with continuous development of new use cases. View the complete list of VisionAI Apps here. If you require a specific scenario not listed here, feel free to contact us.
Our primary focus is on workplace health and safety models, but we are expanding our scope to include Quality Inspection, Food Safety/Debris Detection, and more. These additional scenarios are available to customers on a case-by-case basis.
Install Docker Engine and Docker tools
Open a terminal window and run the following commands to install Docker Engine, Docker CLI, Docker Compose, and Docker Buildx plugin:
Grant permissions to Docker
Run the following command to grant permissions to Docker:
VisionAI application uses Docker containers to run the apps and the Docker images are large in size. Minimum of 100GB of free space is requied on the host machine.
VisionAI application requires minimum 16GB RAM to run the apps.
VisionAI is a Video-based AI platform that uses GPU for inference. It is recommended to use a GPU with at least 8GB of memory for optimal performance. VisionAI supports NVIDIA GPUs only - Following are a few recommended options:
- NVIDIA GeForce RTX 2060/RTX 2060 Ti
- NVIDIA GeForce RTX 3050/RTX 3050 Ti
- NVIDIA P40
- NVIDIA A100
During initial download and setup portion of VisionAI application, we would need good internet connectivity in order to download the required dependencies and Docker containers. Once the setup is complete, VisionAI can be used offline.
You can purchase license by contacing us: firstname.lastname@example.org
For any queries related to VisionAI toolkit usage: email@example.com
- Upgrade pip to the latest version
- Install VisionAI through
- Update to the latest version, if already installed:
- Initialize VisionAI to download and install dependencies (Docker, Pytorch, NVIDIA Triton, etc.):
- Upon successful initialization, you should be able to see the following services running:
|VisionAI API service
|Triton Model server (http)
|Triton Model server (grpc)
|Triton Model metrics server (prometheus)
|Redis server, currently supports PUBSUB
VisionAI Web Application¶
VisionAI supports a web-based option for managing cameras, scenarios.
You can manage cameras, scenarios, see events etc., directly on the web-app. The web-app is running your own local compute instance. All the data is saved in your machine, and it is persistent as long as VisionAI application is not uninstalled.
VisionAI web-app is a software application that runs in a web browser. It is designed to provide a user-friendly interface and functionality that can be accessed from any device, without the need for installation on the device.
Open http://localhost:3001 in the browser. Use your default username/password as master/master. After this, you will be asked to create a new admin user. Please use a strong password and create an admin user.
Once you are signed in, you will see a blank dashboard page. Let’s add an IP camera to the system. In order to do this, go to “Cameras” tab on the left menu and Click on the + button.
A new pop-up window will appear to add cameras. You can enter the camera name, description, and RTSP URI for the camera. The RTSP URI can be obtained from the Camera or NVR documentation. You can ignore the other fields as they are optional. Click on “Add” button.
Once you have added the camera, it should appear on the Cameras window and should show the initial streaming for the camera. Add any additional cameras in a similar fashion. Once all cameras have been added, the front-screen should look like this:
We can enable Vision AI scenarios for each of these cameras. In order to do this, go to Scenarios tab on the left menu to browse through the available scenarios.
This shows details about the Scenario, the model version used, events supported and model accuracy, recall and F1 score metrics. You can now click on the “Get this” button again to apply the scenario to cameras.
In the next page, Select the Cameras for which you want to apply this scenario.
Click "Save and Next". In the next page, you can specify email and text message notification settings. Provide the email address and phone number you want to be notified at and click "Save and Next button"
On the next page, it will ask you to confirm your settings. Click Submit. VisionAI will now download these models and start running them for these scenarios.
- VisionAI supports out-of-box integration with Redis, Prometheus, Grafana and Azure Event Hub. Once the web-app is started, you can view the Grafana dashboard at: http://localhost:3003. The default username and password is
Congratulations! You have successfully configured and used VisionAI toolkit. You can also browse through our scenarios section to understand different use-cases that are supported currently. If you have a need for a scenario, do not hesitate to submit a request here.