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Abstract

What can we learn about a scene by watching it for months or years? A video recorded over a long timespan will depict interesting phenomena at multiple timescales, but identifying and viewing them presents a challenge. The video is too long to watch in full, and some occurrences are too slow to experience in real-time, such as glacial retreat. Timelapse videography is a common approach to summarizing long videos and visualizing slow timescales. However, a timelapse is limited to a single chosen temporal frequency, and often appears flickery due to aliasing and temporal discontinuities between frames. In this paper, we propose Video Temporal Pyramids, a technique that addresses these limitations and expands the possibilities for visualizing the passage of time. Inspired by spatial image pyramids from computer vision, we developed an algorithm that builds video pyramids in the temporal domain. Each level of a Video Temporal Pyramid visualizes a different timescale; for instance, videos from the monthly timescale are usually good for visualizing seasonal changes, while videos from the one-minute timescale are best for visualizing sunrise or the movement of clouds across the sky. To help explore the different pyramid levels, we also propose a Video Spectrogram to visualize the amount of activity across the entire pyramid, providing a holistic overview of the scene dynamics and the ability to explore and discover phenomena across time and timescales. To demonstrate our approach, we have built Video Temporal Pyramids from ten outdoor scenes, each containing months or years of data. We compare Video Temporal Pyramid layers to naive timelapse and find that our pyramids enable alias-free viewing of longer-term changes. We also demonstrate that the Video Spectrogram facilitates exploration and discovery of phenomena across pyramid levels, by enabling both overview and detail-focused perspectives.

Bibtex

@inproceedings{Swift2020TemporalPyramids,
    title = {Visualizing the Passage of Time with Video Temporal Pyramids},
    author = {Melissa E. Swift and Wyatt Ayers and Sophie Pallanck and Scott Wehrwein},
    booktitle = {IEEE VIS: Visualization and Visual Analytics}
    month = {October},
    year = {2022}
} 

Acknowledgements

This work was supported in part by NASA Award NNX15AJ98H under the Washington NASA Space Grant Consortium, and in part by the National Science Foundation under Grant No. 2105372. The Washington NASA Space Grant Consortium is funded by the NASA Office of Stem Engagement. The authors wish to thank Ann Tseng and Richie Mohan for their early contributions.