Human Falling Detection and Tracking

Using Tiny-YOLO oneclass to detect each person in the frame and use AlphaPose to get skeleton-pose and then use ST-GCN model to predict action from every 30 frames of each person tracks.

Which now support 7 actions: Standing, Walking, Sitting, Lying Down, Stand up, Sit down, Fall Down.

Prerequisites

• Python > 3.6
• Pytorch > 1.3.1

Original test run on: i7-8750H CPU @ 2.20GHz x12, GeForce RTX 2070 8GB, CUDA 10.2

Data

This project has trained a new Tiny-YOLO oneclass model to detect only person objects and to reducing model size. Train with rotation augmented COCO person keypoints dataset for more robust person detection in a variant of angle pose.

For actions recognition used data from Le2i Fall detection Dataset (Coffee room, Home) extract skeleton-pose by AlphaPose and labeled each action frames by hand for training ST-GCN model.

Pre-Trained Models

• Tiny-YOLO oneclass - .pth, .cfg
• SPPE FastPose (AlphaPose) - resnet101, resnet50
• ST-GCN action recognition - tsstg

Basic Use

1. Download all pre-trained models into ./Models folder.
2. Run main.py

    # 실시간 실행
    python main_tinyYolo.py \
      -C 0 \
      --device ${cpu or gpu or mps(for apple-silicon)} \
      --phone_num ${phone-number}
    
    # 영상으로 실행하기
    python main_tinyYolo.py \
      -C ${video file path} \
      --device ${cpu or gpu or mps(for apple-silicon)} 

Reference

• AlphaPose : https://github.com/Amanbhandula/AlphaPose
• ST-GCN : https://github.com/yysijie/st-gcn