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PawPal

Eric Hofesmann, Aman Kumar Jha, Preet Gill, Vihang Agarwal

Entertaining and Training your dog while you are away.

Using state-of-the-art computer vision algorithms, this dog localization and activity recognition system can determine what your dog is doing from a home surveillance camera.

Results

Biting C3D Recogntiion Accuracy (%)

Mean Standard Deviation Random Chance
68.41 6.10 50.00

Across 5 splits given in tfrecords_pawpal/split.npy in the dataset download link below

Usage

Requirements

Python 3.5

OpenCV

Tensorflow 1.0.0

Cython

Darkflow for Yolo

M-PACT Activity Recognition Platform

Detailed installation instructions below.

Installation and Setup

Follow instructions below to install darkflow and PawPal

git clone https://github.com/thtrieu/darkflow
virtualenv -p python3.5 env
source env/bin/activate
pip install tensorflow==1.0.0 
pip install Cython 
pip install opencv-python
cd darkflow
sudo apt-get install python3 python-dev python3-dev \
     build-essential libssl-dev libffi-dev \
     libxml2-dev libxslt1-dev zlib1g-dev \
     python-pip
pip install -e .
flow  (ignore any errors)
cd ..
git clone https://github.com/ehofesmann/PawPal/
cd PawPal

Download the weights for C3D Download link

Download yolo.weights Download link

mv ~/Downloads/checkpoint-532.npy ./c3d/
mv ~/Downloads/yolo.weights ../darkflow/bin/

Update the /path/to/darkflow in detect_video.py

python detect_video.py --vidpath example/example1.mp4

Testing

python detect_video.py --vidpath example/example1.mp4

Training or Finetuneing

Dataset

Dog biting vs non biting tfrecords dataset Download link

Activity Recognition Model

Install M-PACT and copy PawPal/c3d/c3d_frozen into the models directory of M-PACT