Computer Vision

Computer vision systems for detection, recognition, and automated visual analysis.

We engineer systems that can see and understand visual data — from detecting objects in live video feeds to recognizing faces for attendance systems. If your project involves cameras, images, or video, we've probably worked on something similar.

What We've Built

Computer vision isn't theoretical for us — we've shipped real projects that run in production environments. Here are the kinds of things we develop:

Object detection systems that identify and monitor items in live
Face recognition for attendance, access control, and verification
People and vehicle counting for traffic analysis and crowd administerment
Smoke and fire detection for safety monitoring
Image processing pipelines for quality inspection and classification
Autonomous Guided Vehicle (AGV) vision systems
Video analytics dashboards with alerts and reporting

How We Approach Computer Vision

Every vision project starts with understanding what you need to detect, how accurate it needs to be, and what environment it'll run in. A smoke detector in a factory has very different requirements than a people counter in a shopping mall.

OpenCV & Deep Learning

We use OpenCV as our foundation for image processing — it's rapid, well-tested, and handles everything from basic transformations to complex feature extraction. For tasks that need deep learning (like object detection or face recognition), we use models built with TensorFlow or PyTorch, often fine-tuned on your specific data for better accuracy.

Real-Time Processing

Many computer vision applications need to work in live — processing video frames as they come in, not hours later. We optimize our solutions for speed, whether that means running on edge devices, using GPU acceleration, or designing streamlined processing pipelines that keep up with live camera feeds.

From Camera to Dashboard

Detection is only half the story. You also need to see results, get alerts, and monitor trends over time. We engineer the full pipeline — from camera input to a web dashboard where it is possible to monitor detections, review footage, and export reports. Everything connected, everything accessible.

Frequently Asked Questions

In most cases, yes. We work with standard IP cameras, RTSP streams, USB cameras, and even smartphone cameras. As long as the video quality is reasonable for the task (resolution, frame rate, lighting), we can usually work with what you already have.

Accuracy depends on several factors — lighting conditions, camera angle, object size, and how much training data is available. For well-defined use cases with good data, we typically achieve high accuracy. We'll run tests with your actual environment and give you realistic numbers before committing to a full deployment.

It depends on the workload. Uncomplicated detection tasks can run on a regular computer. Live video processing with deep learning models usually benefits from a GPU. For edge deployments, we can optimize models to run on devices like NVIDIA Jetson. We'll recommend the right hardware based on your performance needs and budget.

Absolutely. Custom object detection is one of the things we do most. You provide sample images or video of what you need detected, we label the data and train a model exclusively for your use case. The more examples it is possible to provide, the better the model will perform.

Computer Vision

What We've Built

How We Approach Computer Vision

OpenCV & Deep Learning

Real-Time Processing

From Camera to Dashboard

Frequently Asked Questions

Can your computer vision solutions work with our existing cameras?

How accurate is object detection?

Does it need special hardware to run?

Can you train a model to detect something specific to our business?

Talk to Our Experts