lee-man/goturn-pytorch

GOTURN tracking implemented in PyTorch

GOTURN-PyTorch

PyTorch implementation of "Learning to Track at 100 FPS with Deep Regression Networks"

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Table of Contents

• About the Project
• Getting Started
  • Code Setup
  • Data Download
  • Training
  • Testing
• Tracking-Demo

About The Project

This repository contains the reimplementation of GOTURN in PyTorch. If you are interested in the following, you should consider using this repository

• Understand different moving parts of the GOTURN algorithm, independently through code.

• Plug and play with different parts of the pipeline such as data, network, optimizers, cost functions.

• Built with following frameworks:

  • PyTorch-Lightning
  • Visdom

• Similar to original implementation by the author, you will be able to train the model in a day

Tracking Demo

Face	Surfer	Face

Bike	Bear

Getting Started

Code Setup

# Clone the repository
$ git clone https://github.com/nrupatunga/goturn-pytorch

# install all the required repositories
$ cd goturn-pytorch/src
$ pip install -r requirements.txt

# Add current directory to environment
$ source settings.sh

Data Download

cd goturn-pytorch/src/scripts
$ ./download_data.sh /path/to/data/directory

Training on custom dataset

• If you want to use your own custom dataset to train, you might want to understand the how current dataloader works. For that you might need a smaller dataset to debug, which you can find it here. This contains few samples of ImageNet and Alov dataset.

• Once you understand the dataloaders, please refer here for more information on where to modify training script.

Training

$ cd goturn-pytorch/src/scripts

# Modify the following variables in the script
# Path to imagenet
IMAGENET_PATH='/media/nthere/datasets/ISLVRC2014_Det/'
# Path to alov dataset
ALOV_PATH='/media/nthere/datasets/ALOV/'
# save path for models
SAVE_PATH='./caffenet/'

# open another terminal and run
$ visdom
# You can visualize the train/val images, loss curves,
# simply open https://localhost:8097 in your browser to visualize

# training
$ bash train.sh

Loss

Testing

In order to test the model, you can use the model in this link or you can use your trained model

$ mkdir goturn-pytorch/models

# Copy the extracted caffenet folder into models folder, if you are
# using the trained model

$ cd goturn-pytorch/src/scripts
$ bash demo_folder.sh

# once you run, select the bounding box on the frame using mouse, 
# once you select the bounding box press 's' to start tracking, 
# if the model lose tracking, please press 'p' to pause and mark the
# bounding box again as before and press 's' again to continue the
# tracking


# To test on a new video, you need to extract the frames from the video
# using ffmpeg or any other tool and modify folder path in
# demo_folder.sh accordingly

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Known issues

• Model trained is Caffe is better in getting the objectness, this way it handles some of the cases quite well than the PyTorch model I have trained. I tried to debug this issue. You can refer to the discussion here. If someone solves this issue, please open a PR