01CAMERA / SCENE CALIBRATION
Optical & Scene Scoping
We analyze camera placement, frame rates, lighting conditions, privacy boundaries, and target visual events before selecting a model architecture.
ENGINEERING / FROM CAMERA TO APPLICATION OUTPUT
Engineered to see.
Built for the edge.
We architect computer vision and Edge AI systems end to end—making latency, model precision, privacy boundaries, and hardware constraints explicit at every stage.
HIRZEXAVISION ARCHITECTURE / 01
VISION ENGINEERING PIPELINE · CAMERA TO DASHBOARD01 — 06
01 / INPUTCamera / SensorOptical & RTSP video streams
02 / INGESTVideo ProcessingOpenCV · Frame decoding & prep
03 / PERCEPTIONComputer Vision ModelsYOLO · PyTorch · Detection & tracking
04 / EDGE AIEdge InferenceONNX Runtime · TensorRT optimization
05 / REASONINGEvent IntelligenceScene logic · Risk scoring & rules
06 / OUTPUTApplication / DashboardFastAPI · Live UI, alerts & APIs
EDGE-TO-CLOUD TELEMETRY & OBSERVABILITYLow-latency local inference · Event sync · Model iteration ↺
01 / VISION ENGINEERING METHODOLOGY
From camera frame
to actionable edge intelligence.
Reliable computer vision is an end-to-end engineering discipline—connecting frame capture, model optimization, edge deployment, and operator software.
02PYTORCH / YOLO / OPENCV
Vision Model Engineering
We train, fine-tune, and benchmark object detection, multi-object tracking, and activity recognition models using PyTorch, YOLO, and OpenCV.
03ONNX RUNTIME / TENSORRT
Edge Optimization & Runtime
We export and quantize models for ONNX Runtime and TensorRT (when supported) to achieve low-latency, on-device inference within hardware constraints.
04FASTAPI / DOCKER / DASHBOARD
Event APIs & Monitoring UI
We connect edge inference pipelines to FastAPI services, Dockerized deployments, device communication APIs, and live operator dashboards.
Interactive Vision Pipeline
From camera sensor to live application output.
Inspect each stage of our six-step computer vision and Edge AI architecture.
01 / PIPELINE STAGEHIRZEXA VISION
Camera / Sensor Input
Ingest live video streams from IP cameras, optical sensors, or local RTSP endpoints with resilient reconnection, frame pacing, and exposure handling.
KEY TECHNOLOGIES & OUTPUTRTSP / Optical Sensor Streams
VISION & EDGE ENGINEERING STACK
Production stack for real-time perception.
We combine proven computer vision frameworks, edge inference runtimes, and modern backend services.
Python · PyTorch · YOLO
Training, fine-tuning, and evaluating object detection, multi-object tracking, and activity recognition models.
OpenCV · Frame Pipelines
Real-time RTSP/optical stream ingestion, frame decoding, spatial calibration, and region-of-interest filtering.
ONNX Runtime · TensorRT
Model quantization, graph optimization, and low-latency local inference on supported edge accelerators.
FastAPI · Device Communication APIs
Asynchronous event ingestion, device-to-cloud telemetry, webhook alerts, and operator control APIs.
Docker · Hybrid Edge-Cloud
Reproducible packaging for on-premise edge appliances and scalable cloud inference services.
Monitoring Dashboards & Mobile UI
Real-time visual overlays, event timelines, assisted risk scoring, and human-in-the-loop alert workflows.
ARCHITECTURAL PRINCIPLES / WHAT WE HOLD CONSTANT
Benchmark on real scenes.
We evaluate detection, tracking, and false-positive rates against real camera angles, lighting conditions, and edge hardware budgets.
Edge efficiency & privacy by design.
Processing video streams locally at the edge reduces latency, conserves bandwidth, and keeps sensitive visual feeds within defined boundaries.
Human-in-the-loop intelligence.
Our vision software and alerts are built to surface structured context and assist human operators—never to replace human judgment.