Computer Vision for images and videos
From the production line to the point of sale, from video surveillance to medical imaging: we bring computer vision models into production, even on edge devices with tight latency, cost, and power constraints.
Image and video recognition for quality control, security, retail, and industrial analysis.
Use cases
- Automated quality control in production
- People counting and retail heatmaps
- Perimeter security and anomaly detection
- ID document OCR and KYC
- Sports analysis and tactical AI
Measurable benefits
- 100% inspection (vs manual sampling)
- Reduction of post-sale discarded defects
- Insights into customer behavior in-store
- Operation even offline / edge
Technical details
Models
- YOLOv8/v9 for real-time detection
- Segment Anything (SAM) for segmentation
- CLIP for visual search and zero-shot
- Custom fine-tuned models
Video pipelines
- RTSP/WebRTC streaming
- Multi-object tracking (ByteTrack, DeepSORT)
- Re-identification
- Temporal anomaly detection
Edge & deployment
- NVIDIA Jetson, Coral, Raspberry Pi
- INT8 quantization for latency/W
- ONNX, TensorRT, OpenVINO
- OTA model updates
Privacy
- Automatic face / license plate blurring
- On-device processing (no cloud)
- GDPR and DPIA compliance
- Access audit logs
FAQ
How many images are needed to train a model?
With transfer learning, 500-2,000 labeled images per class are often enough. We also provide data labeling services.
Does it work in real-time?
Yes, with the right models (YOLO, MobileNet) we reach 30-60 FPS even on edge hardware.
Can I use my existing cameras?
Yes, any RTSP/ONVIF/HTTP stream can be integrated. We suggest upgrades only if necessary.