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Channel sifted model for pose estimation

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Abstract

In recent years, human pose estimation, also called keypoint estimation, has been a research spot in the field of computer vision. However, most methods focus on how to achieve high accuracy and ignore the computational cost. These methods usually use deeper layers and more complex structures to construct their networks result in a surge in computational cost, so it is difficult to apply them in real environments with real-time requirements. Some methods try to reduce the computational cost by decreasing precision of parameter or using simple structures. However, they can only achieve low performance, so it is difficult to apply in the real environments. Inspired by lightweight network design, this paper proposes a channel sifted method for human pose estimation, which is called the channel sifted network (CSN). A lightweight ResNet is used as the backbone, and a channel scoring module (CSM) is applied. Both parts aim to balance the computational cost and prediction accuracy of the network. The lower the computational cost, the faster the inference speed. In the experimental part, we first train and test the network on the COCO2017 dataset and MPII dataset respectively, and then demonstrate the effectiveness of lightweight backbone and CSM in the network through ablation study. Compared with other lightweight models, CSN achieves higher performance.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61972056, in part by the Hunan Provincial Natural Science Foundation of China under Grant 2021JJ30743 and 2021JJ30741, in part by the Degree & Post-graduate Education Reform Project of Hunan Province of China under Grant 2020JGZD043, in part by the Youth Innovation Project of Guangdong Provincial Department of Education under Grant 2020kqncx205.

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  1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China

    Shuren Zhou & Liang Peng

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  1. Shuren Zhou
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Correspondence to Shuren Zhou.

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Zhou, S., Peng, L. Channel sifted model for pose estimation. Appl Intell 53, 11373–11388 (2023). https://doi.org/10.1007/s10489-022-04091-1

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