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Automate threat and risk detection from security camera feeds

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Spot Real Problems in Real-Time

From package theft to large chemical spills, computer vision can detect objects both big and small
Theft and Loss Prevention
Slip and Fall Hazards
Package Theft
Abandoned Luggage

Produce a trained model in hours, not weeks

FEELINGSS AI seamlessly integrates with the most popular labeling services and training tools.

Extract Value From Your Existing Video Feeds

If you are capturing real-time video from your security cameras and other surveillance equipment, you already have enough data to train your own computer vision model. Start extracting valuable information from the footage you are already collecting.

Instantly Detect Objects in Frame

When it comes to monitoring risk and identifying threats, every second counts. A well-trained computer vision model can detect objects instantly with great accuracy. Save time and money by automating these monitoring tasks.

Continuously Improve Your Model After Deployment

As a centralized management tool for your datasets, FEELINGSS AI allows you to deploy your model once and scale it as you collect more data. Easily detect additional objects, adjust configurations, and experiment with different labeling and training services.

Improve Model Quality With Less Data

FEELINGSS AI is specifically designed to get the most out of the data you already collected
Static Crop
If activity only occurs in a portion of the camera frame, you can crop your images to only show the active areas. This can dramatically speed up training time by cutting out excess pixels.
Augmentations
Generate augmented versions of your source images to simulate different lighting conditions, camera angles, or contrast settings. Create more training data without uploading more images.
Version Control
Dataset versions allow you to conduct rapid experimentation with various labeling and training tools. Adjust settings, generate export, and go.
Advanced Health Check
Helpful charts that provide insight into the quality of your dataset and how to improve it including a breakdown of class balance, dimension insights, and an annotation heatmap.