Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping
** PyTorch implementation of our paper titled Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping, Image and Vision Computing, 2020
Anil Sharma, Saket Anand, Sanjit K. Kaul
In this repository, we are sharing code for our IMAVIS paper that extends our ICAPS paper. We use deep Q-learning with n-step bootstrapping to learn a policy for camera selections.
The code is organized as follows:
scripts
All scripts are provided as python notebooks.data
It contains data files for all datasets (all 4 sets of NLPR MCT, Duke). It also contains scripts to read these data files and also the training testing split as used in our ICAPS paper. Original images of the dataset can be downloaded from the dataset website.
Download the dataset from the [official website] . This dataset contains four sub-datasets and we have used all four in our method.
We have converted all datasets into trajectory files and only these are used by our method. These are placed in data
folder and necessary scripts are provided to read the files and training/testing split.
The code requires pytorch 1.1.0 and jupyter notebook (should work well with Python 3.5). Apart from it, you need to install scipy, matplotlib, numpy and hickle for loading and plotting purposes.
The pre-trained model and results for each specific case of the simulation are provide in 'models' .
In 'plots', you will find various scripts to reproduce all tables and figures reported in the paper. Use MCT evaluation kit to generate MCTA values from the results file. Result files are kept in results_icaps
folder for every case.
To train a model from scratch, use any of the notebooks to train DQN policy for NLPR set-4. To train for a different dataset, change db_no
in the notebook accordingly.
If this code helps your research, please cite the following work which made it possible.
@inproceedings{sharmaScheduleCameras,
title = {Reinforcement Learning based Querying in Camera Networks for Efficient Target Tracking},
author = {Sharma, Anil and Anand, Saket and Kaul, Sanjit},
booktitle = {ICAPS},
year = {2019}
}
@article{Sharma2020IntelligentQF,
title={Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping},
author={Anil Sharma and Saket Anand and Sanjit Krishnan Kaul},
journal={ArXiv},
year={2020},
volume={abs/2004.09632}
}
This code is licensed under CC BY-NC 4.0. Some external dependencies have their own license.