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Sensors 2019, 19(3), 718; https://doi.org/10.3390/s19030718

Improved Convolutional Pose Machines for Human Pose Estimation Using Image Sensor Data

Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
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Received: 5 January 2019 / Revised: 31 January 2019 / Accepted: 5 February 2019 / Published: 10 February 2019
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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Abstract

In recent years, increasing human data comes from image sensors. In this paper, a novel approach combining convolutional pose machines (CPMs) with GoogLeNet is proposed for human pose estimation using image sensor data. The first stage of the CPMs directly generates a response map of each human skeleton’s key points from images, in which we introduce some layers from the GoogLeNet. On the one hand, the improved model uses deeper network layers and more complex network structures to enhance the ability of low level feature extraction. On the other hand, the improved model applies a fine-tuning strategy, which benefits the estimation accuracy. Moreover, we introduce the inception structure to greatly reduce parameters of the model, which reduces the convergence time significantly. Extensive experiments on several datasets show that the improved model outperforms most mainstream models in accuracy and training time. The prediction efficiency of the improved model is improved by 1.023 times compared with the CPMs. At the same time, the training time of the improved model is reduced 3.414 times. This paper presents a new idea for future research. View Full-Text
Keywords: human pose estimation; convolutional pose machines; GoogLeNet; fine-tuning; image sensor human pose estimation; convolutional pose machines; GoogLeNet; fine-tuning; image sensor
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Qiang, B.; Zhang, S.; Zhan, Y.; Xie, W.; Zhao, T. Improved Convolutional Pose Machines for Human Pose Estimation Using Image Sensor Data. Sensors 2019, 19, 718.

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