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An information fusion framework for person localization via body pose in spectator crowds
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Shaban, Muhammad, Mahmood, Arif, Al-Maadeed, Somaya Ali and Rajpoot, Nasir M. (2019) An information fusion framework for person localization via body pose in spectator crowds. Information Fusion, 51 . pp. 178-188. doi:10.1016/j.inffus.2018.11.011
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Official URL: http://dx.doi.org/10.1016/j.inffus.2018.11.011
Abstract
Person localization or segmentation in low resolution crowded scenes is important for person tracking and recognition, action detection and anomaly identification. Due to occlusion and lack of inter-person space, person localization becomes a difficult task. In this work, we propose a novel information fusion framework to integrate a deep head detector and a body pose detector. A more accurate body pose showing limb positions will result in more accurate person localization. We propose a novel Deep Head Detector (DHD) to detect person heads in crowds. The proposed DHD is a fully convolutional neural network and it has shown improved head detection performance in crowds. We modify Deformable Parts Model (DPM) pose detector to detect multiple upper body poses in crowds. We efficiently fuse the information obtained by the proposed DHD and the modified DPM to obtain a more accurate person pose detector. The proposed framework is named as Fusion DPM (FDPM) and it has exhibited improved body pose detection performance on spectator crowds. The detected body poses are then used for more accurate person localization by segmenting each person in the crowd.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Computer vision, Crowds, Form perception, Image segmentation, Multisensor data fusion | ||||||||
| Journal or Publication Title: | Information Fusion | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 1566-2535 | ||||||||
| Official Date: | November 2019 | ||||||||
| Dates: |
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| Volume: | 51 | ||||||||
| Page Range: | pp. 178-188 | ||||||||
| DOI: | 10.1016/j.inffus.2018.11.011 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
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