Real-Time Human Pose Estimation via Cascaded Neural Networks Embedded with Multi-task Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-07-28

AUTHORS

Satoshi Tanabe , Ryosuke Yamanaka , Mitsuru Tomono , Makiko Ito , Teruo Ishihara

ABSTRACT

Deep convolutional neural networks (DCNNs) have recently been applied to Human pose estimation (HPE). However, most conventional methods have involved multiple models, and these models have been independently designed and optimized, which has led to sub-optimal performance. In addition, these methods based on multiple DCNNs have been computationally expensive and unsuitable for real-time applications. This paper proposes a novel end-to-end framework implemented with cascaded neural networks. Our proposed framework includes three tasks: (1) detecting regions which include parts of the human body, (2) predicting the coordinates of human body joints in the regions, and (3) finding optimum points as coordinates of human body joints. These three tasks are jointly optimized. Our experimental results demonstrated that our framework improved the accuracy and the running time was 2.57 times faster than conventional methods. More... »

PAGES

241-252

References to SciGraph publications

Book

TITLE

Computer Analysis of Images and Patterns

ISBN

978-3-319-64697-8
978-3-319-64698-5

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-64698-5_21

DOI

http://dx.doi.org/10.1007/978-3-319-64698-5_21

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090965495


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