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Practical Motion Segmentation for Urban Street View Scenes | IEEE Conference Publication | IEEE Xplore

Practical Motion Segmentation for Urban Street View Scenes


Abstract:

Though a long-studied problem, motion segmentation has yet to migrate into practical applications. We argue that a vital step towards that goal lies in addressing motion ...Show More

Abstract:

Though a long-studied problem, motion segmentation has yet to migrate into practical applications. We argue that a vital step towards that goal lies in addressing motion segmentation for the specific setting of interest. To this end, this paper presents a new approach for image-based motion segmentation in the case of vehicles navigating inside an urban environment. We exploit two application-specific factors - the restricted camera movement and the known type of moving objects - to deal with the two major limiting factors - missing data and strong perspective effects - that affect most previous “generic” motion segmentation algorithms. By constraining the geometry and exploiting known semantic classes in the scene, we achieve much higher accuracy than previous approaches. In addition to the novel algorithm, we contribute a more realistic motion segmentation benchmark dataset for moving platforms by annotating real video sequences from the KITTI dataset. Experiments on this dataset and other synthetic data confirm the effectiveness of the proposed approach.
Date of Conference: 21-25 May 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2577-087X
Conference Location: Brisbane, QLD, Australia

References

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