id: 05978241
dt: a
an: 05978241
au: Pan, Guodong; Wong, Kwan-Yee Kenneth
ti: Multi-body segmentation and motion number estimation via over-segmentation
    detection.
so: Koch, Reinhard (ed.) et al., Computer vision ‒ ACCV 2010 workshops. ACCV
    2010 international workshops, Queenstown, New Zealand, November 8‒9,
    2010. Revised selected papers, Part II. Berlin: Springer (ISBN
    978-3-642-22818-6/pbk). Lecture Notes in Computer Science 6469, 194-203
    (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-22819-3_20
ab: Summary: This paper studies the problem of multi-body segmentation and
    motion number estimation. It is well known that motion number plays a
    critical role in the success of multi-body segmentation. Most of the
    existing methods exploit only motion affinity to segment and determine
    the number of motions. Motion number estimated in this way is often
    seriously affected by noise. In this paper, we recast the problem of
    multi-body segmentation and motion number estimation into an
    over-segmentation detection problem, and introduce three measures,
    namely loss of spatial locality (LSL), split ratio (SR) and cluster
    distance (CD), for over-segmentation detection. A hierarchical
    clustering method based on motion affinity is applied to split the
    motion clusters recursively until over-segmentation occurs.
    Over-segmentation is detected by Kernel Support Vector Machines trained
    under supervised learning using the above three measures. We leverage
    on Hopkins155 database to test our method and, with the same motion
    affinity measure, our method outperforms another state-of-the-art
    method. To the best of our knowledge, this paper is the first to tackle
    the problem of multi-body segmentation and motion number estimation
    from the perspective of over-segmentation detection.
rv: