TY - JOUR
T1 - Detecting moving regions in CrowdCam images
AU - Dafni, Adi
AU - Moses, Yael
AU - Avidan, Shai
AU - Dekel, Tali
N1 - Publisher Copyright:
© 2017 Elsevier Inc.
PY - 2017/7
Y1 - 2017/7
N2 - 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.
AB - 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.
KW - CrowdCam
KW - Epipolar geometry
KW - Motion detection
UR - http://www.scopus.com/inward/record.url?scp=85018947613&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2017.04.004
DO - 10.1016/j.cviu.2017.04.004
M3 - מאמר
AN - SCOPUS:85018947613
VL - 160
SP - 36
EP - 44
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
ER -