An efficient sparse pruning method for human pose estimation.
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| Title: | An efficient sparse pruning method for human pose estimation. |
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| Authors: | Wang, Mingyang (AUTHOR), Sun, Tianyi (AUTHOR), Song, Kang (AUTHOR), Li, Shuang (AUTHOR), Jiang, Jing (AUTHOR), Sun, Linjun (AUTHOR) |
| Source: | Connection Science. Dec2022, Vol. 34 Issue 1, p960-974. 15p. |
| Subjects: | Computer vision, Human beings |
| Abstract: | Human pose estimation (HPE) is crucial for computer vision (CV). Moreover, it's a vital step for computers to understand human actions and behaviours. However, the huge number of parameters and calculations in the HPE model have brought big challenges to deploy to resource-constrained mobile devices. Aiming to overcome the challenge, we propose a sparse pruning method (SPM) for the HPE model. First, L1 regularisation is added in the training phase of the original model, and network parameters of the convolution layers (CLs) and batch normalisation layers (BNLs) are sparsely trained to obtain a network structure with sparse weights. We then combine the sparse weights of filters with the scaling parameters of the BNLs to determine their importance. Finally, the structured pruning method is used to prune the sparse filters and corresponding channels. SPM can reduce the number of model parameters and calculations without affecting precision. Promising results indicate that SPM outperforms other advanced pruning methods. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Human pose estimation (HPE) is crucial for computer vision (CV). Moreover, it's a vital step for computers to understand human actions and behaviours. However, the huge number of parameters and calculations in the HPE model have brought big challenges to deploy to resource-constrained mobile devices. Aiming to overcome the challenge, we propose a sparse pruning method (SPM) for the HPE model. First, L1 regularisation is added in the training phase of the original model, and network parameters of the convolution layers (CLs) and batch normalisation layers (BNLs) are sparsely trained to obtain a network structure with sparse weights. We then combine the sparse weights of filters with the scaling parameters of the BNLs to determine their importance. Finally, the structured pruning method is used to prune the sparse filters and corresponding channels. SPM can reduce the number of model parameters and calculations without affecting precision. Promising results indicate that SPM outperforms other advanced pruning methods. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09540091 |
| DOI: | 10.1080/09540091.2021.2012423 |
