Summary of Mousesis: a Frames-and-events Dataset For Space-time Instance Segmentation Of Mice, by Friedhelm Hamann et al.
MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice
by Friedhelm Hamann, Hanxiong Li, Paul Mieske, Lars Lewejohann, Guillermo Gallego
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of tracking and segmenting objects in videos, particularly under degraded conditions or during fast movements. Despite recent progress, existing algorithms struggle with these challenges. The authors introduce a new task called space-time instance segmentation, which aims to segment instances throughout the entire duration of the sensor input. A dataset, , is also introduced, containing annotated ground-truth labels (pixel-level instance segmentation masks) of up to seven freely moving and interacting mice. The authors provide two reference methods, showing that leveraging event data can consistently improve tracking performance, especially when combined with conventional cameras. The results highlight the potential of event-aided tracking in difficult scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us track and identify objects in videos, even when they’re moving fast or the video is blurry. Right now, computers have trouble doing this when the conditions are tough. The authors create a new way to do this called space-time instance segmentation, which means breaking down the video into tiny moments and identifying what’s happening in each one. They also created a special dataset with lots of examples of mice moving around and interacting with each other. This can help computers learn how to track objects more accurately. |
Keywords
» Artificial intelligence » Instance segmentation » Tracking
