On Premise IP Camera analysis

Developer Architecture Overview: Camera Input to Cloud Inference with On-Prem App

This document outlines the architecture for applications that use local IP camera input, process frames on-premises, and send selected data to the cloud for inference and alerting. This design is optimized for reliability, user control, and extensibility in real-world deployments.

System Components

1. IP Camera

• Role: Provides a real-time video feed.

• Protocol Support: RTSP or HTTP streams.

• Deployment Note: Can be any standard IP camera compatible with the network environment.

2. [On-Prem] App

A lightweight local application deployed on a local machine (e.g., embedded PC, NUC, or industrial gateway) with three primary modules:

a. App Core

• Manages communication between all subsystems.

• Handles application lifecycle, settings, and local network services.

• Serves as the bridge to cloud and notification layers.

b. Frame Capture & Sampling Logic

• Connects to the IP camera stream.

• Extracts frames at regular intervals or based on motion/logic triggers.

• Prepares images for secure transmission to the cloud.

c. Local Dashboard

• Web- or Electron-based UI accessible on the local network.

• Displays real-time camera feed (optionally), recent detections, logs, and configuration tools.

• Allows for manual review and flagging of archived images.

3. [Cloud] Inference via EyePop.ai API

• Role: Processes each sampled image using a selected EyePop.ai model.

• Security: Images are never stored server-side—processing is stateless.

• Response: Returns structured JSON with inference results (e.g., bounding boxes, classifications, tags).

4. [Cloud] Notification Service

• Integration Options: Twilio (SMS), SendGrid (email), Slack, Webhooks, or any third-party service.

• Trigger: Upon receiving actionable inference results (e.g., person detected after hours), the app sends alert payloads to cloud notification services.

5. [Optional] Local Image Archive

• Stores selected images locally for:

  • Regulatory compliance or auditing

  • Dataset refinement and future model training

  • Manual review by operators

• Integrated with the Local Dashboard for search, tagging, and export.

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Data Flow Summary

1. Capture: The IP camera sends a continuous stream to the local app.

2. Sampling: The app extracts and filters frames using internal logic.

3. Inference: Sampled frames are sent to EyePop.ai for analysis.

4. Result Handling: Inference results are used to trigger alerts and log events.

5. User Interaction: A dashboard provides visibility, settings control, and image review.

6. Archiving: Optionally, images are stored locally based on rules or user actions.

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Benefits

• Low Latency Local Interface: Dashboard and logic run on-site.

• Cloud Offload: Heavy inference is handled remotely with zero storage overhead.

• Flexible Notification: Pluggable alerting channels tailored to use case.

• Audit Ready: Image archive supports regulated environments or continuous learning loops.