:movie_camera: :running: :running: Suspicious-Activity-Detection-Using-Pose-Estimation

📝 Description

• This implemantation is based on official AlphaPose Pose Estimation Algorithm.
• It is ‘AlphaPose’ & ‘XGBOOST’ based “Suspicious-Activity-Detection-Using-Pose Estimation” project.
• Purpose of this project is to make a system which can detect if someone is trying to Climb a house compound wall, Climbing on Fence, Climbing on gate & trying to do some suspicious activity.
• This model will detect this activities accurately & helps to prevent those kind of activities by giving real time feedback.

🎯 Inference demo

🏽‍ 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.

:desktop_computer: Installation

:hammer_and_wrench: Requirements

• Python 3.5+
• Cython
• PyTorch 1.1+
• torchvision 0.3.0+
• Linux
• GCC<6.0, check https://github.com/facebookresearch/maskrcnn-benchmark/issues/25

:gear: Setup on Colab

1. Install PyTorch :-

   
   $ !pip3 install torch==1.1.0 torchvision==0.3.0

2. Git Clone :-
```bash
$ !git clone https://github.com/akshaykadam771/Suspicious-Activity-Detection-Using-Pose-Estimation.git 

3. Install :-

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

```bash
$ !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

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. Output Images & json file will save in bydefault **AlphaPose/examples/res** folder.

3. Testing with **Videos**  :-
```bash
$ !python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --video examples/video/demo5.mp4 --outdir examples/res --save_video --gpus 0

3. If it is giving memory error during Videos testing you can add --sp argument in command which enable Single processing :-

   
   $ !python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --video examples/video/demo5.mp4 --outdir examples/res --save_video --gpus 0 --sp

## :open_file_folder: Json Dataset for training your own custom ML model :wrench: :nut_and_bolt: :hammer:   

- **Drive Link** :- https://drive.google.com/file/d/1sTJkWBmuE6iBi_mCAs1DJ-KR6MnoZD7-/view?usp=sharing
- This CSV file contaning 17 keypoints (Total 17x2=34 ) of Human body part as columns for each individual person while performing this 2 activities :-                **1) Climbing  2)Standing**
- **Action** :- 0 = Climbing & 1 = Standing