pose-estimation-detection

Pose estimation & detection has been minimally implemented using the OpenPose implementation https://github.com/ildoonet/tf-pose-estimation with Tensorflow. For the binary classification of poses, namely the classes : sitting or standing, the model used, MobileNet (a CNN originally trained on the ImageNet Large Visual Recognition Challenge dataset), was retrained (final layer) on a dataset of ~1500 images of poses.

The model is able to estimate the human poses as well as classify the current pose to a fairly good degree of accuracy.

Demo

An alternative for improving the model along with deep learning is to include heuristics, in the form of calculation of the skeletal points’ relative distances from each other.

FPS & estimation/detection varies with the CPU/GPU power.

Training Examples

For sitting pose
For standing pose

Dependencies

The following are required :

python3
tensorflow 1.9.0 (works well even with CPU version)
opencv3
slim
slidingwindow
- https://github.com/adamrehn/slidingwindow

Cloning & installing dependencies

$ git clone https://github.com/SyBorg91/pose-estimation-detection
$ cd pose-estimation-detection
$ pip3 install -r requirements.txt

Pose Estimation with realtime webcam feed

$ python run_webcam.py --model=mobilenet_thin --resize=432x368 --camera=0

Run the above command to start pose estimation with the onboard webcam.

References

satyaborg / pose-estimation-detection Goto Github PK