FEVA: Fast Event Video Annotation Tool

Snehesh Shrestha, William Sentosatio, Huaishu Peng, Cornelia Fermüller, Yiannis Aloimonos

Perception and Robotics Group

University of Maryland College Park


Paper

Code

Demo

Tutorials

Abstract

Video Annotation is a crucial process in computer science and social science alike. Many video annotation tools (VAT) offer a wide range of features for making annotation possible. We conducted an extensive survey of over 59 VAT and interviewed interdisciplinary researchers to evaluate the usability of the VAT. Our findings suggest that most current VAT have overwhelming user interfaces, poor interaction techniques, and difficult-to-understand features. These often lead to longer annotation time, label inconsistencies, and user fatigue. We introduce FEVA, a video annotation tool with streamlined interaction techniques and a dynamic interface that makes labeling tasks easy and fast. FEVA focuses on speed, accuracy, and simplicity to make annotation quick, consistent, and straightforward. For example, annotators can control the speed and direction of the video and mark the onset and the offset of a label in real time with single key presses. In our user study, FEVA users, on average, require 36% less interaction than the most popular annotation tools (Advene, ANVIL, ELAN, VIA, and VIAN). The participants (N=32) rated FEVA as more intuitive and required less mental demand.

Acknowledgments

We thank Chethan Parameshwara, Levi Burner, Lindsay Little, and peers from UMD and the Perception and Robotics Group for their valuable feedback and discussions. We extend special thanks to all our project contributors Johnny Chiu, Rachelle Sims, John Gao, Leya Abraham, Vikram Sehgal, Swagata Chakroborty, Lucas Stuart, and Lin Chen. The support of NSF under grant OISE 2020624 is greatly acknowledged.

Bibtex

If you use this tool or code in your research, please cite our work:

@inproceedings{shrestha2022feva,
  title     = {{FEVA}: {F}ast {E}vent {V}ideo {A}nnotation Tool},
  author    = {Snehesh Shrestha, William Sentosatio, Huiashu Peng, 
                Cornelia Fermüller, Yiannis Aloimonos},
  year      = {2022},
}

License

FEVA is freely available for non-commercial and research use and may be redistributed under the conditions detailed on the license page. For commercial licensing or if you have any questions, please get in touch with me at snehesh@umd.edu.