SSDPose: A Single Shot Deep Pose Estimation and Analysis

Abobakr, A; Abdelkader, H; Iskander, J; Nahavandi, D; Saleh, K; Attia, M; Hossny, M; Nahavandi, S

SSDPose: A Single Shot Deep Pose Estimation and Analysis

Abobakr, A Abdelkader, H Iskander, J Nahavandi, D Saleh, K Attia, M Hossny, M Nahavandi, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 1862-1868
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Abobakr, A
dc.contributor.author	Abdelkader, H
dc.contributor.author	Iskander, J
dc.contributor.author	Nahavandi, D
dc.contributor.author	Saleh, K
dc.contributor.author	Attia, M
dc.contributor.author	Hossny, M
dc.contributor.author	Nahavandi, S
dc.date	2019-10-06
dc.date.accessioned	2020-06-02T23:09:32Z
dc.date.available	2020-06-02T23:09:32Z
dc.date.issued	2019
dc.identifier.citation	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 1862-1868
dc.identifier.isbn	978-1-7281-4569-3
dc.identifier.issn	2577-1655
dc.identifier.uri	http://hdl.handle.net/10453/141077
dc.description.abstract	Human posture estimation is a fundamental challenge in computer vision research. This is a task that has received substantial interest due to the importance of evaluating the human performance in several disciplines. The ultimate goal for the vision-based pose estimation task is the markerless accurate prediction of necessary postural information. This paper proposes a single shot deep human posture detection and estimation network. The proposed SSDPose architecture increments standard object detection networks to feature posture estimation. SSDPose is an end-to-end trainable model that detects and estimates the body posture from a single image. Further, our network has been trained to predict joint angles which are essential information for several domains such as biomechanic and ergonomic posture analysis. The reference joint angles have been generated using motion capture sequences and a novel inverse kinematics method. Experimental results demonstrate that SSDPose effectively detects and estimates the posture by achieving person mean average precision (mAP) of 98.2%, an average joint angles MAE of 3.16 ± 1.23 deg and an RMSE of 4.22 ± 1.73 deg at up to 30 FPS.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
dc.relation.ispartof	IEEE International Conference on Systems, Man and Cybernetics
dc.relation.isbasedon	10.1109/SMC.2019.8913964
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	SSDPose: A Single Shot Deep Pose Estimation and Analysis
dc.type	Conference Proceeding
utslib.citation.volume	2019-October
utslib.location.activity	Bari, Italy
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-02T23:09:24Z
pubs.finish-date	2019-10-09
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-10-06
pubs.volume	2019-October
utslib.start-page	1862
dc.location	Piscataway, USA

Abstract:

Human posture estimation is a fundamental challenge in computer vision research. This is a task that has received substantial interest due to the importance of evaluating the human performance in several disciplines. The ultimate goal for the vision-based pose estimation task is the markerless accurate prediction of necessary postural information. This paper proposes a single shot deep human posture detection and estimation network. The proposed SSDPose architecture increments standard object detection networks to feature posture estimation. SSDPose is an end-to-end trainable model that detects and estimates the body posture from a single image. Further, our network has been trained to predict joint angles which are essential information for several domains such as biomechanic and ergonomic posture analysis. The reference joint angles have been generated using motion capture sequences and a novel inverse kinematics method. Experimental results demonstrate that SSDPose effectively detects and estimates the posture by achieving person mean average precision (mAP) of 98.2%, an average joint angles MAE of 3.16 ± 1.23 deg and an RMSE of 4.22 ± 1.73 deg at up to 30 FPS.

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http://hdl.handle.net/10453/141077