Detecting moving regions in CrowdCam images

Adi Dafni, Yael Moses, Shai Avidan, Tali Dekel

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)36-44
Number of pages9
JournalComputer Vision and Image Understanding
Volume160
DOIs
StatePublished - Jul 2017

Keywords

  • CrowdCam
  • Epipolar geometry
  • Motion detection

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