1 research outputs found
CrowdCam: Dynamic Region Segmentation
We consider the problem of segmenting dynamic regions in CrowdCam images,
where a dynamic region is the projection of a moving 3D object on the image
plane. Quite often, these regions are the most interesting parts of an image.
CrowdCam images is a set of images of the same dynamic event, captured by a
group of non-collaborating users. Almost every event of interest today is
captured this way. This new type of images raises the need to develop new
algorithms tailored specifically for it. We propose an algorithm that segments
the dynamic regions in CrowdCam images. The proposed algorithm combines cues
that are based on geometry, appearance and proximity. First, geometric
reasoning is used to produce rough score maps that determine, for every pixel,
how likely it is to be the projection of a static or dynamic scene point. These
maps are noisy because CrowdCam images are usually few and far apart both in
space and in time. Then, we use similarity in appearance space and proximity in
the image plane to encourage neighboring pixels to be labeled similarly as
either static or dynamic. We define an objective function that combines all the
cues and solves it using an MRF solver. The proposed method was tested on
publicly available CrowdCam datasets, as well as a new and challenging dataset
we collected. Our results are better than the current state-of-the-art