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How to label each tracked animal's motion state in a video with JEV Sees

Assign stationary, walking, running, or uncertain to every tracked animal in a clip.

  • Research
  • Intermediate
  • 30 minutes
  • From the README
  • Any agent

Authorization notice. JEV Sees is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Credentials. Keep your JEV API key out of shared scripts and screenshots of the terminal output.

Who it is forA wildlife researcher or pet owner with a camera recording who wants a timeline of how an animal is moving.

The problemWatching long clips to note when an animal stops, walks, or runs takes a lot of time and is easy to get wrong.

The outcomeA list of frames with object IDs, labels, and a motion choice with confidence for each sampled frame.

What you need first

  • Python 3.10 or newer
  • A JEV API key from console.typesafe.ai/keys
  • Internet access for the first run, unless the Florence-2 and CLIP weights are already available locally
  • Git and a terminal

Steps

  1. Clone the project with git clone https://github.com/CharlesFeng0314/JEV_sees.git.
  2. Enter the folder with cd JEV_sees.
  3. Install it with python -m pip install -e ..
  4. Set your key on macOS or Linux with export TYPESAFE_API_KEY="your-key", or in PowerShell with $env:TYPESAFE_API_KEY = "your-key".
  5. Run the bundled example with python examples/animal_motion.py.

Prompt for your agent

In the JEV_sees repo at <repo path>, with TYPESAFE_API_KEY set, run examples/animal_motion.py on assets/black_cat_walking.mp4. Report frame_index, video_time_s, object_id, and choice for the output; verify that every choice is one of stationary, walking, running, or uncertain.

Caveat. The README examples use sample media, and the default sampling rate is about twice per second, so fast motion between samples may be missed.

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