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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded


Month Degree Awarded


First Advisor

Erik Learned-Miller

Subject Categories

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces


The ability to recognize motion is one of the most important functions of our visual system. Motion allows us both to recognize objects and to get a better understanding of the 3D world in which we are moving. Because of its importance, motion is used to answer a wide variety of fundamental questions in computer vision such as: (1) Which objects are moving independently in the world? (2) Which objects are close and which objects are far away? (3) How is the camera moving?
My work addresses the problem of moving object segmentation in unconstrained videos. I developed a probabilistic approach to segment independently moving objects in a video sequence, connecting aspects of camera motion estimation, relative depth and flow statistics. My work consists of three major parts:

  • Modeling motion using a simple (rigid) motion model strictly following the principles of perspective projection and segmenting the video into its different motion components by assigning each pixel to its most likely motion model in a Bayesian fashion.
  • Combining piecewise rigid motions to more complex, deformable and articulated objects, guided by learned semantic object segmentations.
  • Learning highly variable motion patterns using a neural network trained on synthetic (unlimited) training data. Training data is automatically generated strictly following the principles of perspective projection. In this way well-known geometric constraints are precisely characterized during training to learn the principles of motion segmentation rather than identifying well-known structures that are likely to move.
This work shows that a careful analysis of the motion field not only leads to a consistent segmentation of moving objects in a video sequence, but also helps us understand the scene geometry of the world we are moving in.


Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.