| CPC G06V 10/44 (2022.01) [G06T 7/73 (2017.01); G06V 10/82 (2022.01); G06V 40/11 (2022.01); G06T 2207/20084 (2013.01); G06T 2207/30196 (2013.01)] | 18 Claims |
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1. A hand posture estimation method, comprising:
obtaining an initial feature map corresponding to a hand region in a candidate image;
obtaining a fused feature map by performing feature fusion processing on the initial feature map; wherein the feature fusion processing is configured to fuse features around a plurality of key points;
obtaining a target feature map by performing deconvolution processing on the fused feature map; wherein the deconvolution processing is configured to adjust a resolution of the fused feature map; and
obtaining coordinate information of the plurality of key points based on the target feature map to determine a posture estimation result of the hand region in the candidate image;
wherein the obtaining a fused feature map by performing feature fusion processing on the initial feature map comprises:
obtaining a first feature map by performing a first convolution processing on the initial feature map through a first convolutional network; wherein the first convolution processing is configured to extract local detail information of the plurality of key points; and
wherein before the performing a first convolution processing on the initial feature map through a first convolutional network, the method further comprises:
obtaining a dimensionality-reduced feature map by performing dimensionality reduction processing on the initial feature map; wherein the dimensionality reduction processing is configured to reduce the number of channels of the initial feature map; and
obtaining the first feature map by performing the first convolution processing on the dimensionality-reduced feature map through the first convolutional network.
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