Machine Learning

Machine learning is a branch of artificial intelligence where software learns patterns from data instead of being explicitly programmed with rules. In video analytics, it is the technique that lets a system recognize a face, read a license plate, or detect smoke without a developer coding every case.

How It Works

A machine learning workflow has three phases:

1. Training. The algorithm is fed thousands to millions of labeled examples (images of faces, plates, objects) and adjusts its internal parameters to minimize prediction errors.
2. Validation. Separate data tests that the model generalizes to new examples rather than memorizing the training set.
3. Inference. The trained model is deployed to production and applied to live video in milliseconds per frame.

Classical machine learning used hand-crafted features and algorithms like decision trees, SVMs, and random forests. Modern video analytics predominantly uses deep learning, a subfield of machine learning based on multi-layer neural networks.

Why It Matters

Machine learning removed the ceiling that rule-based software hit in video surveillance:

• Adaptability — models retrain on new data to handle new camera angles, lighting, or object types.
• Scale — one model serves thousands of cameras with identical logic.
• Accuracy — state-of-the-art models exceed human performance on many narrow vision tasks.

The entire VEZHA platform from IncoreSoft runs on trained machine learning models, each specialized for one task — face recognition, ALPR, fire detection, and more.

Use Cases

• Face and license plate recognition for security and access control
• Smoke, fire, and anomaly detection for industrial and public safety
• Traffic, retail, and workplace analytics for operations
• Predictive alerts that learn normal patterns and flag deviations

Frequently Asked Questions

What's the difference between AI, machine learning, and deep learning?

AI is the broad goal of making machines act intelligently. Machine learning is one technique to achieve it. Deep learning is a specific type of machine learning using neural networks with many layers. All three terms are often used interchangeably in marketing, but technically they nest inside each other.

Can machine learning work offline?

Yes. Once trained, a machine learning model runs entirely offline. Edge deployments in IncoreSoft's platform run inference on-site without internet connectivity.

How much data is needed to train a useful model?

It depends on the task. Narrow object detectors can work with a few thousand labeled examples; production face recognition typically uses millions. Transfer learning — fine-tuning an existing model — reduces requirements dramatically.