Estimation of Missing Human Body Parts via Bidirectional LSTM

Ibrahim Hamed Ismail RADWAN; Akshay Asthana; Hafsa ISMAIL; Byron Keating; Roland GOECKE

doi:10.1109/FG.2019.8756597

Estimation of Missing Human Body Parts via Bidirectional LSTM

Ibrahim Hamed Ismail RADWAN, Akshay Asthana, Hafsa ISMAIL, Byron Keating, Roland GOECKE

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this paper, a bi-directional long-short term memory (LSTM) based approach is proposed for the estimation of missing body parts in a human pose estimation context. Accurate human pose estimation is often a key component for accurate human action and activity recognition. The key idea of our algorithm is to learn the temporal consistencies of the human body poses between previous and subsequent frames. This helps in estimating missing body parts and improves the general smoothness of the pose detection results. The approach acts as a post-processing step after the application of any off-the-shelf body part detector and has been evaluated on the PoseTrack dataset for both validation and testing sequences. The results show consistent improvement in the detection across all body parts.

Original language	English
Title of host publication	Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
Place of Publication	Danvers, USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	443-447
Number of pages	5
ISBN (Electronic)	9781728100890
ISBN (Print)	9781728100906
DOIs	https://doi.org/10.1109/FG.2019.8756597
Publication status	Published - 2019
Event	14th IEEE International Conference on Automatic Face and Gesture Recognition - Lille, France Duration: 14 May 2019 → 18 May 2019 http://fg2019.org/

Publication series

Name	Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019

Conference

Conference	14th IEEE International Conference on Automatic Face and Gesture Recognition
Abbreviated title	FG 2019
Country/Territory	France
City	Lille
Period	14/05/19 → 18/05/19
Internet address	http://fg2019.org/

Access to Document

10.1109/FG.2019.8756597

Cite this

RADWAN, I. H. I., Asthana, A., ISMAIL, H., Keating, B., & GOECKE, R. (2019). Estimation of Missing Human Body Parts via Bidirectional LSTM. In Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) (pp. 443-447). [8756597] (Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FG.2019.8756597

RADWAN, Ibrahim Hamed Ismail ; Asthana, Akshay ; ISMAIL, Hafsa et al. / Estimation of Missing Human Body Parts via Bidirectional LSTM. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). Danvers, USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 443-447 (Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019).

@inproceedings{9294e4cfd85d43919fde1f374430590b,

title = "Estimation of Missing Human Body Parts via Bidirectional LSTM",

abstract = "In this paper, a bi-directional long-short term memory (LSTM) based approach is proposed for the estimation of missing body parts in a human pose estimation context. Accurate human pose estimation is often a key component for accurate human action and activity recognition. The key idea of our algorithm is to learn the temporal consistencies of the human body poses between previous and subsequent frames. This helps in estimating missing body parts and improves the general smoothness of the pose detection results. The approach acts as a post-processing step after the application of any off-the-shelf body part detector and has been evaluated on the PoseTrack dataset for both validation and testing sequences. The results show consistent improvement in the detection across all body parts.",

keywords = "Human pose estimation, Bidirectional LSTM",

author = "RADWAN, {Ibrahim Hamed Ismail} and Akshay Asthana and Hafsa ISMAIL and Byron Keating and Roland GOECKE",

note = "Funding Information: This research was funded in part by the Australian Research Council (ARC) Linkage Project grant LP160100910. Moreover, we acknowledge the support of NVIDIA for granting us a Titan-XP GPU device that helped us to implement our experiments. Funding Information: Acknowledgment: This research was funded in part by the Australian Research Council (ARC) Linkage Project grant LP160100910. Moreover, we acknowledge the support of NVIDIA for granting us a Titan-XP GPU device that helped us to implement our experiments. Publisher Copyright: {\textcopyright} 2019 IEEE.; 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019 ; Conference date: 14-05-2019 Through 18-05-2019",

year = "2019",

doi = "10.1109/FG.2019.8756597",

language = "English",

isbn = "9781728100906",

series = "Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "443--447",

booktitle = "Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)",

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url = "http://fg2019.org/",

}

RADWAN, IHI, Asthana, A, ISMAIL, H, Keating, B & GOECKE, R 2019, Estimation of Missing Human Body Parts via Bidirectional LSTM. in Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)., 8756597, Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, IEEE, Institute of Electrical and Electronics Engineers, Danvers, USA, pp. 443-447, 14th IEEE International Conference on Automatic Face and Gesture Recognition, Lille, France, 14/05/19. https://doi.org/10.1109/FG.2019.8756597

Estimation of Missing Human Body Parts via Bidirectional LSTM. / RADWAN, Ibrahim Hamed Ismail; Asthana, Akshay; ISMAIL, Hafsa et al.
Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). Danvers, USA: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 443-447 8756597 (Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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T1 - Estimation of Missing Human Body Parts via Bidirectional LSTM

AU - RADWAN, Ibrahim Hamed Ismail

AU - Asthana, Akshay

AU - ISMAIL, Hafsa

AU - Keating, Byron

AU - GOECKE, Roland

N1 - Funding Information: This research was funded in part by the Australian Research Council (ARC) Linkage Project grant LP160100910. Moreover, we acknowledge the support of NVIDIA for granting us a Titan-XP GPU device that helped us to implement our experiments. Funding Information: Acknowledgment: This research was funded in part by the Australian Research Council (ARC) Linkage Project grant LP160100910. Moreover, we acknowledge the support of NVIDIA for granting us a Titan-XP GPU device that helped us to implement our experiments. Publisher Copyright: © 2019 IEEE.

PY - 2019

Y1 - 2019

N2 - In this paper, a bi-directional long-short term memory (LSTM) based approach is proposed for the estimation of missing body parts in a human pose estimation context. Accurate human pose estimation is often a key component for accurate human action and activity recognition. The key idea of our algorithm is to learn the temporal consistencies of the human body poses between previous and subsequent frames. This helps in estimating missing body parts and improves the general smoothness of the pose detection results. The approach acts as a post-processing step after the application of any off-the-shelf body part detector and has been evaluated on the PoseTrack dataset for both validation and testing sequences. The results show consistent improvement in the detection across all body parts.

AB - In this paper, a bi-directional long-short term memory (LSTM) based approach is proposed for the estimation of missing body parts in a human pose estimation context. Accurate human pose estimation is often a key component for accurate human action and activity recognition. The key idea of our algorithm is to learn the temporal consistencies of the human body poses between previous and subsequent frames. This helps in estimating missing body parts and improves the general smoothness of the pose detection results. The approach acts as a post-processing step after the application of any off-the-shelf body part detector and has been evaluated on the PoseTrack dataset for both validation and testing sequences. The results show consistent improvement in the detection across all body parts.

KW - Human pose estimation

KW - Bidirectional LSTM

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UR - http://www.mendeley.com/research/estimation-missing-human-body-parts-via-bidirectional-lstm

U2 - 10.1109/FG.2019.8756597

DO - 10.1109/FG.2019.8756597

M3 - Conference contribution

SN - 9781728100906

T3 - Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019

SP - 443

EP - 447

BT - Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Danvers, USA

T2 - 14th IEEE International Conference on Automatic Face and Gesture Recognition

Y2 - 14 May 2019 through 18 May 2019

ER -

RADWAN IHI, Asthana A, ISMAIL H, Keating B, GOECKE R. Estimation of Missing Human Body Parts via Bidirectional LSTM. In Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). Danvers, USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 443-447. 8756597. (Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019). doi: 10.1109/FG.2019.8756597

Estimation of Missing Human Body Parts via Bidirectional LSTM

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