# A Detailed Rubric for Motion Segmentation

arXiv: https://arxiv.org/abs/1610.10033 · HTML: https://mlsanity.com/paper/1610.10033/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [Self-supervised Video Object Segmentation by Motion Grouping](https://mlsanity.com/paper/2104.07658/) (2021)
- [Segmenting Moving Objects via an Object-Centric Layered Representation](https://mlsanity.com/paper/2207.02206/) (2022)
- [Opening up Open-World Tracking](https://mlsanity.com/paper/2104.11221/) (2021)
- [The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields](https://mlsanity.com/paper/2203.00115/) (2022)
- [Made to Order: Discovering monotonic temporal changes via self-supervised video ordering](https://mlsanity.com/paper/2404.16828/) (2024)
- [RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions](https://mlsanity.com/paper/2109.01820/) (2021)

## Abstract (arXiv)

Motion segmentation is currently an active area of research in computer Vision. The task of comparing different methods of motion segmentation is complicated by the fact that researchers may use subtly different definitions of the problem. Questions such as "Which objects are moving?", "What is background?", and "How can we use motion of the camera to segment objects, whether they are static or moving?" are clearly related to each other, but lead to different algorithms, and imply different versions of the ground truth. This report has two goals. The first is to offer a precise definition of motion segmentation so that the intent of an algorithm is as well-defined as possible. The second is to report on new versions of three previously existing data sets that are compatible with this definition. We hope that this more detailed definition, and the three data sets that go with it, will allow more meaningful comparisons of certain motion segmentation methods.