Neural Network

A neural network is a computational model loosely inspired by the human brain. It consists of layers of interconnected nodes ("neurons") that learn to transform input data — such as a video frame — into a useful output, like a detection, classification, or identity.

How It Works

Every neural network has three types of layers:

1. Input layer. Accepts the raw data. For a video frame, this is the pixel array (e.g., 1920×1080×3 for RGB).
2. Hidden layers. Perform learned transformations. Convolutional layers detect local patterns (edges, textures, shapes); pooling layers reduce dimensionality; fully connected layers combine features for the final decision.
3. Output layer. Produces the final answer — a class label, a bounding box, or a feature embedding.

During training, the network adjusts millions of internal weights to minimize prediction errors on labeled examples. Once trained, it runs inference on new frames in milliseconds.

Why It Matters

Neural networks are what make modern video analytics practical. They replaced decades of hand-engineered feature detectors with models that learn directly from data:

• Generalization — one architecture handles faces from all ages, ethnicities, and angles.
• Scalability — same model runs on thousands of cameras in parallel.
• Continuous improvement — retraining on new data improves performance without re-coding rules.

IncoreSoft's face recognition engine uses deep neural networks trained on millions of images to achieve up to 99.35% accuracy.

Use Cases

• Face recognition — CNN-based embeddings for identity matching
• License plate recognition — object detection + character recognition networks
• Fire and smoke detection — specialized vision transformers
• Pose estimation — keypoint detection networks for fall detection
• Audio classification — 1D networks for glass-break or gunshot detection

Frequently Asked Questions

What's the difference between a neural network and deep learning?

"Deep learning" refers specifically to neural networks with many hidden layers. Shallow neural networks (1–2 layers) existed for decades; the depth is what enables today's performance on images and video.

How big are modern neural networks?

Vision models range from a few million parameters (MobileNet, suitable for edge devices) to hundreds of millions or billions (vision transformers). There's always a trade-off between accuracy, latency, and hardware cost.

Can neural networks run on the camera itself?

Yes. Modern smart cameras include neural accelerators (NPUs) that run compact models at 30+ FPS. IncoreSoft supports edge deployment for low-latency, privacy-preserving analytics.