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gratheon / entrance-observer

Beehive entrance video processing service. Manages video inferencing. Intended to be deployed on edge on NVidia Jetson Orin or NVidia Jetson Nano

object_counting_output.mov

Features

  • Uses 4K USB video camera stream as input, stores it into 10 sec chunks
    • Tried dual CSI cameras too, it could work too, but quality was not sufficient
  • Uploads video chunks to gratheon web-app for playback
  • Runs bee detection

Installation & Usage

git clone https://github.com/Gratheon/entrance-observer.git
python3 -m pip install -r requirements.txt
API_TOKEN=... BOX_ID=... && python3 video_camera_server.py

Architecture

Video chunk upload for observation & playback

See video_camera_server.py

flowchart LR
	beehive-entrance-video-processor --"upload video chunk"--> gate-video-stream
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Edge Inference (TODO)

We separate webcam from inference mostly because inference is dockerized while webcam uses local window for preview.

flowchart LR
	subgraph Edge
	beehive-entrance-video-processor --"read with native python to file"--> webcam
	beehive-entrance-video-processor --"run inference from file" --> models-bee-detector[<a href="https://github.com/Gratheon/models-bee-detector">models-bee-detector</a>]
	end

	subgraph Cloud
	 beehive-entrance-video-processor --"send edge-inference results"--> telemetry-api[<a href="https://github.com/Gratheon/telemetry-api">telemetry-api</a>]
	end
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Distributed GPU inference assistance (TODO)

flowchart LR

	subgraph Edge
	beehive-entrance-video-processor --"inference unprocessed file" --> models-gate-tracker
	end

	subgraph Cloud
	beehive-entrance-video-processor --"get next unprocessed video segment"--> gate-video-stream
	beehive-entrance-video-processor --"send inference results"--> gate-video-stream -- "store results long-term" --> mysql

	end
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Beehive entrance video processing service on the edge

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