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How to show live space-by-space occupancy from existing cameras with Lotvulture

Turn existing lot cameras into a live occupancy map with per-space status updates.

Credentials. Camera RTSP URLs often embed usernames and passwords, so store them only in the app and avoid pasting them into shared logs or agent chats.

Who it is forA facilities manager or lot operator who needs to see which spaces are free right now and has cameras already covering the lot.

The problemAttendants give delayed estimates, and in-ground sensors are costly to install and maintain.

The outcomeA browser dashboard at localhost:8000 that shows each drawn parking space as occupied or free, updating over WebSockets.

What you need first

  • One or more IP, RTSP, or snapshot-capable cameras with a reachable stream URL or snapshot endpoint
  • A Windows PC for the native installer, or a Linux host with Docker for the container
  • Access to the Releases page or the ghcr.io container image
  • A Developer Cloud API key from lotvulture.com, only if using the cloud API use case

Steps

  1. Install Lot Vulture: on Windows, run the LotVulture_Setup_*.exe from the Releases page; on Linux, run docker run -d --name lotvulture --restart unless-stopped -p 8000:8000 -v lotvulture-data:/var/lib/lotvulture ghcr.io/lotvulture/lotvulture:latest. (inferred, check the README)
  2. Open http://localhost:8000 in a browser.
  3. In the Cameras view, add each RTSP stream URL or snapshot endpoint.
  4. Use the visual canvas editor to map the four corners of each parking space.
  5. Open the live dashboard and confirm each mapped space shows a state.

Prompt for your agent

Using the repo lotvulture/lotvulture, set up Lot Vulture with the Docker command from its README on a Linux host, then add the camera stream <camera_rtsp_url> in the Cameras view. Report the occupancy state of each mapped space shown on the live dashboard at http://localhost:8000, and verify that the states change when a vehicle enters or leaves a mapped space.

Caveat. The Community Edition runs a bundled standard model on optimized CPU, and GPU acceleration and the high-accuracy model require a Commercial license.

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