Realtime motion segmentation based multibody visual SLAM
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Histogram-based description of local space-time appearance
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Mobile surveillance by 3D-outlier analysis
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Multi-body segmentation and motion number estimation via over-segmentation detection
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Joint estimation of segmentation and structure from motion
Computer Vision and Image Understanding
Interactive object modelling based on piecewise planar surface patches
Computer Vision and Image Understanding
Application of heterogenous motion models towards structure recovery from motion
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Multibody structure from motion (SfM) is the extension of classical SfM to dynamic scenes with multiple rigidly moving objects. Recent research has unveiled some of the mathematical foundations of the problem, but a practical algorithm which can handle realistic sequences is still missing. In this paper, we discuss the requirements for such an algorithm, highlight theoretical issues and practical problems, and describe how a static structure-from-motion framework needs to be extended to handle real dynamic scenes. Theoretical issues include different situations in which the number of independently moving scene objects changes: Moving objects can enter or leave the field of view, merge into the static background (e.g., when a car is parked), or split off from the background and start moving independently. Practical issues arise due to small freely moving foreground objects with few and short feature tracks. We argue that all of these difficulties need to be handled online as structure-from-motion estimation progresses, and present an exemplary solution using the framework of probabilistic model-scoring.