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An information fusion framework for person localization via body pose in spectator crowds.

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Title: An information fusion framework for person localization via body pose in spectator crowds.
Authors: Shaban, Muhammad1,2 (AUTHOR) m.shaban@warwick.ac.uk, Mahmood, Arif1,2,3 (AUTHOR) arif.mahmood@itu.edu.pk, Al-Maadeed, Somaya Ali2 (AUTHOR) s_alali@qu.edu.qa, Rajpoot, Nasir1 (AUTHOR) n.m.rajpoot@warwick.ac.uk
Source: Information Fusion. Nov2019, Vol. 51, p178-188. 11p.
Subjects: Spectators, Crowds, Anomaly detection (Computer security), Artificial neural networks
Abstract: • In low resolution crowd images, each person is localized and segmented. • For head detection in crowds, a novel Deep-CNN based head detector (DHD) is proposed. • DPM pose detection algorithm is modified to detect upper body poses in crowds. • DHD and modified DPM are integrated using a novel fusion framework. • Information fusion improved person pose detection and segmentation results. 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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• In low resolution crowd images, each person is localized and segmented. • For head detection in crowds, a novel Deep-CNN based head detector (DHD) is proposed. • DPM pose detection algorithm is modified to detect upper body poses in crowds. • DHD and modified DPM are integrated using a novel fusion framework. • Information fusion improved person pose detection and segmentation results. 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. [ABSTRACT FROM AUTHOR]
ISSN:15662535
DOI:10.1016/j.inffus.2018.11.011