Estimation of Missing Human Body Parts via Bidirectional LSTM

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

Research output: A Conference proceeding or a Chapter in BookConference contribution

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 languageEnglish
Title of host publicationProceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
Place of PublicationDanvers, USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages443-447
Number of pages5
ISBN (Electronic)9781728100890
ISBN (Print)9781728100906
DOIs
Publication statusPublished - 2019
Event14th IEEE International Conference on Automatic Face and Gesture Recognition - Lille, France
Duration: 14 May 201918 May 2019
http://fg2019.org/

Publication series

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

Conference

Conference14th IEEE International Conference on Automatic Face and Gesture Recognition
Abbreviated titleFG 2019
CountryFrance
CityLille
Period14/05/1918/05/19
Internet address

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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). Danvers, USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FG.2019.8756597