This repository contains the code for the paper "Using Visual Anomaly Detection for Task Execution Monitoring"
See requirements.txt.
The main ones are:
- torch=1.6.0
- pytorch-lightning=0.9.0
git clone --recursive https://github.com/sthoduka/motion_anomaly_detection.git
Follow the instructions here.
python main.py \
--video_root=<path to training data folder> \
--val_video_root=<path to validation data folder> \
--test_video_root=<path to test data folder> \
--sample_size=64 \
--batch_size=128 \
--default_root_dir=<path to tensorboard logs folder> \
--row_log_interval=10 \
--learning_rate=0.0001 \
--max_epochs=50 \
--gpus=1 \
--flow_type=normal_masked \
--prediction_offset_start=5 \
--prediction_offset=9
Follow the instructions here.
The dataset already includes the rendered robot body images. If you want to regenerate them or render them for your own dataset/robot, follow the instructions here.
Please cite this work in your publications if you found it useful. Here is the BibTeX entry:
@inproceedings{thoduka2021using,
title={{Using Visual Anomaly Detection for Task Execution Monitoring}},
author={Thoduka, Santosh and Gall, Juergen and Pl{\"o}ger, Paul G},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={4604--4610},
year={2021},
organization={IEEE}
}