pcaher/Pose-with-Action

🏃 🚶 💃 Pose-With-Action

• This repository represents " Action Recognition Using Alphapose ".
• With the help of this project we can detect the human Actions/Activities based on the Human Pose.

📝 Description

• This implemantation is based on official Alphapose repository https://github.com/MVIG-SJTU/AlphaPose
• In this project we have used Alphapose and XGBOOST for activity recognition.

⏳ Dataset

• Download the dataset for custom training
• https://drive.google.com/drive/folders/1CFxvuigTzbnRXUojFeCRozxjUbYiQ8RM?usp=sharing

🏽‍ Download Object Detection Model

• Download the object detection model manually : yolov3-spp.weights file from following Drive Link
• https://drive.google.com/file/d/1h2g_wQ270_pckpRCHJb9K78uDf-2PsPd/view?usp=sharing
• Download the weight file and Place it into " detector/yolo/data/ " folder.

🏽‍ For Pose Tracking, Download the object tracking model

• For pose tracking, download the object tracking model manually: " JDE-1088x608-uncertainty " from following Drive Link
• https://drive.google.com/file/d/1oeK1aj9t7pTi1u70nSIwx0qNVWvEvRrf/view?usp=sharing
• Download the file and Place it into " detector/tracker/data/ ". folder.

🏽‍ Download Fast.res50.pt file

• Download the " fast.res50.pth " file from following Drive Link
• https://drive.google.com/file/d/1WrvycZnVWwltSa6cjeTznEFOyNAwHEZu/view?usp=sharing
• Download the file and Place it into " pretrained_models/ ". folder.

🖥️ Installation

🛠️ Requirements

• Python 3.5+
• Cython
• PyTorch 1.1+
• torchvision 0.3.0+
• Linux
• GCC<6.0, check facebookresearch/maskrcnn-benchmark#25

⚙️ Setup

1. Install PyTorch :-

$ pip3 install torch==1.1.0 torchvision==0.3.0

2. Install :-

$ export PATH=/usr/local/cuda/bin/:$PATH

$ export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH

$ pip install cython

$ sudo apt-get install libyaml-dev

$ python setup.py build develop --user

$ python -m pip install Pillow==6.2.1

$ pip install -U PyYAML

🎯 Inference demo

1. Testing with Images ( Put test images in AlphaPose/examples/demo/ ) :-

$ python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --indir examples/demo/ --save_img

2. Testing with Video ( Put test video in AlphaPose/examples/demo/ ) :-

$ python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --video examples/demo/3.mp4 --outdir examples/res1 --save_video --gpus 0

📖 Please Go through Pose_With_Action_HLD2.docx for more info.

Contributors

• Akshay Kumar Prasad
• Akshay Namdev Kadam
• Arjun K
• ATUL KUMAR
• Deepak Kumar Behera
• Jerryl Davis
• Kancharla Bharath Kumar
• Karthik P
• Madhavi Patel
• Mukesh
• Oinam Bhobendra
• pamita singh kandari
• Sameer sudhir Deshmukh
• Sasidharan M
• shrinivas kandlikar