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Real-time human pose estimation with convolutional neural networks

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Details

Original languageEnglish
Title of host publicationVISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
PublisherSCITEPRESS
Pages335-342
Number of pages8
Volume5
ISBN (Electronic)9789897582905
DOIs
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventINTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS -
Duration: 1 Jan 1900 → …

Conference

ConferenceINTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS
Period1/01/00 → …

Abstract

In this paper, we present a method for real-time multi-person human pose estimation from video by utilizing convolutional neural networks. Our method is aimed for use case specific applications, where good accuracy is essential and variation of the background and poses is limited. This enables us to use a generic network architecture, which is both accurate and fast. We divide the problem into two phases: (1) pre-training and (2) finetuning. In pre-training, the network is learned with highly diverse input data from publicly available datasets, while in finetuning we train with application specific data, which we record with Kinect. Our method differs from most of the state-of-the-art methods in that we consider the whole system, including person detector, pose estimator and an automatic way to record application specific training material for finetuning. Our method is considerably faster than many of the state-of-the-art methods. Our method can be thought of as a replacement for Kinect in restricted environments. It can be used for tasks, such as gesture control, games, person tracking, action recognition and action tracking. We achieved accuracy of 96.8% (PCK@0.2) with application specific data.

Keywords

  • Convolutional neural networks, Human pose estimation, Person detection

Publication forum classification

Field of science, Statistics Finland