Shaban, Muhammad, Mahmood, Arif, Al-Maadeed, Somaya Ali and Rajpoot, Nasir M. (2018) An information fusion framework for person localization via body pose in spectator crowds. Information Fusion . doi:10.1016/j.inffus.2018.11.011 (In Press)

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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
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of 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:	26 November 2018
Dates:	Date Event 26 November 2018 Available 25 November 2018 Accepted
Date of first compliant deposit:	6 December 2018
DOI:	10.1016/j.inffus.2018.11.011
Status:	Peer Reviewed
Publication Status:	In Press
Access rights to Published version:	Restricted or Subscription Access

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