  We address the novel problem of detecting dynamic regions in CrowdCam images,
a set of still images captured by a group of people. These regions capture the
most interesting parts of the scene, and detecting them plays an important role
in the analysis of visual data. Our method is based on the observation that
matching static points must satisfy the epipolar geometry constraints, but
computing exact matches is challenging. Instead, we compute the probability
that a pixel has a match, not necessarily the correct one, along the
corresponding epipolar line. The complement of this probability is not
necessarily the probability of a dynamic point because of occlusions, noise,
and matching errors. Therefore, information from all pairs of images is
aggregated to obtain a high quality dynamic probability map, per image.
Experiments on challenging datasets demonstrate the effectiveness of the
algorithm on a broad range of settings; no prior knowledge about the scene, the
camera characteristics or the camera locations is required.
