MobileDistortion dataset

To quantify the extent of image distortion in real-world mobile augmented scenarios, we use commodity AR devices, i.e., the Nokia 7.1 smartphone and the MagicLeap One head-mounted AR set, to record videos in different real-world environments (at 30 frames per second). The collected videos comprise the MobileDistortion dataset and can be downloaded via https://1drv.ms/u/s!Aqyf-lNI69G1gkdZUz5J6D5jzv4D?e=nILsiW.

Summary:

• Extract frames from videos
• Citation
• Acknowledgments

The tree structure of the video set is:

Distortion
└───Motion Blur
│   │
│   └───MagicLeap One - MotionBlur Outdoor.mp4
│   └───MagicLeap One - MotionBlur Corridor.mp4
│   └───Nokia7.1 - MotionBlur Outdoor.mp4
│   └───Nokia7.1 - MotionBlur Corridor.mp4
│   
└───Gaussian Blur
│   │
│   └───Nokia7.1 - Foggy.mp4
│   └───Nokia7.1 - Underwater.mp4
|
└───Noise
│   │
│   └───Nokia7.1 - Camera zoom In.mp4
│   └───Nokia7.1 - Dark Room.mp4

1. Extract frames from videos

Follow the procedure below to extract image frames from the videos:

1. Install the open-cv library before running the python script "extract_frames.py".

2. Running the script by: python .\extract_frames.py -source_video

   replace the field -source_video by the PATH of the video from which you want to extract the frames. For instance:
   python .\extract_frames.py .\Distortion\Motion Blur\MotionBlur Outdoor.mp4.

3. You should be able to see the generated folder that contains extracted images in the folder "frames".

2. Citation

Please cite the following paper in your publications if the dataset helps your research.

 @inproceedings{Liu20CollabAR,
  title={{CollabAR}: Edge-assisted collaborative image recognition for mobile augmented reality },
  author={Liu, Zida and Lan, Guohao and Stojkovic, Jovan and Yunfan, Zhang and Joe-Wong, Carlee and Gorlatova, Maria},
  booktitle={Proceedings of the 19th ACM/IEEE Conference on Information Processing in Sensor Networks},
  year={2020}
}

3. Acknowledgments

The authors of this dataset are Zida Liu, Guohao Lan, and Maria Gorlatova. This work was done in the Intelligent Interactive Internet of Things Lab at Duke University.

Contact Information of the contributors:

• zida.liu AT duke.edu
• guohao.lan AT duke.edu
• maria.gorlatova AT duke.edu

This work is supported by the Lord Foundation of North Carolina and by NSF awards CSR-1903136 and CNS-1908051.