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Realtime-Action-Recognition-Based-On-OpenPose-And-TF-Pose-Estimation

A skeleton-based real-time online action recognition project, classifying and recognizing base on framewise joints, which can be used for safety monitoring..
(The code comments are partly descibed in chinese)

Introduction

The pipline of this work is:

  • Realtime pose estimation by OpenPose;
  • Online human tracking for multi-people scenario by DeepSort algorithm;
  • Action recognition with DNN for each person based on single framewise joints detected from Openpose.

Dependencies

  • python >= 3.5
  • Opencv >= 3.4.1
  • sklearn
  • tensorflow & keras
  • numpy & scipy
  • pathlib

Usage

  • Download the openpose VGG tf-model with command line ./download.sh(/Pose/graph_models/VGG_origin) or fork here, and place it under the corresponding folder;
  • python main.py, it will start the webcam. (you can choose to test video with command python main.py --video=test.mp4, however I just tested the webcam mode)
  • By the way, you can choose different openpose pretrained model in script.
    VGG_origin: training with the VGG net, as same as the CMU providing caffemodel, more accurate but slower, mobilenet_thin: training with the Mobilenet, much smaller than the origin VGG, faster but less accurate.
    However, Please attention that the Action Dataset in this repo is collected along with the VGG model running.

Training with own dataset

  • prepare data(actions) by running main.py, remember to uncomment the code of data collecting, the origin data will be saved as a .txt.
  • transforming the .txt to .csv, you can use EXCEL to do this.
  • do the training with the traing.py in Action/training/, remember to change the action_enum and output-layer of model.

Test result

  • actions detection

  • work surveilence

  • multi people

Note

  • Action recognition in this work is framewise based, so it's technically "Pose recognition" to be exactly;
  • Action is actually a dynamic motion which consists of sequential static poses, therefore classifying framewisely is not a good solution.
  • Considering of using RNN(LSTM) model to classify actions with dynamic sequential joints data is the next step to improve this project.

Acknowledge

Thanks to the following awesome works: