A fast C++ implementation of TensorFlow Lite Posenet on a bare Raspberry Pi 4 64-bit OS.
Once overclocked to 1825 MHz, the app runs at 9.4 FPS without any hardware accelerator.
Paper
Frame rate Pose Lite : 9.4 FPS (RPi 4 @ 1825 MHz - 64 bits OS)
Frame rate Pose Lite : 5.0 FPS (RPi 4 @ 2000 MHz - 32 bits OS) see 32-OS
Special made for a Raspberry Pi 4 see Q-engineering deep learning examples
To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/TensorFlow_Lite_Pose_RPi_64-bits/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md
Your MyDir folder must now look like this:
Dance.mp4
posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite
TestTensorFlow_Lite_Pose.cpb
Pose_single.cpp
Run TestTensorFlow_Lite.cpb with Code::Blocks. Remember, you also need a working OpenCV 4 on your Raspberry.
I fact you can run this example on any aarch64 Linux system.
See the movie at: https://www.youtube.com/watch?v=LxSR5JJRBoI