Hourglass Network Key Point Detection via Feature Map Attention
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Solution Overview
Problem
Existing methods for detecting key points of human skeletons in computer vision tasks suffer from low accuracy due to uniform deep-layer learning across feature maps, which fails to account for the varying influence of different feature maps on heat maps, leading to suboptimal pose estimation and behavior prediction.
Innovation Solution
The introduction of an attention mechanism within the pre-trained hourglass network structure, where weight values are set for feature maps to differentiate the degree of deep-layer learning, allowing for adjusted feature learning based on the importance of each feature map, thereby improving the accuracy of heat map generation and key point detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform deep-layer learning is applied to all feature maps, then the processing is simple and fast, but the detection accuracy is low
Solution Approach 1:
The patent applies different learning strategies to different feature maps based on their characteristics. Important feature maps (those with higher correlation to key points) receive more aggressive learning with larger learning rates and deeper processing, while less important maps receive standard processing. This local differentiation resolves the contradiction by improving accuracy through selective enhancement without uniformly increasing complexity across the entire network.
Solution Approach 2:
The patent dynamically adjusts learning rates and processing depth parameters based on the importance of each feature map. By changing these parameters selectively for different feature maps rather than applying uniform parameters, the system achieves higher detection accuracy while maintaining reasonable overall complexity through targeted parameter optimization.
2Measurement precision
If deep-layer feature learning is performed on all feature maps with equal intensity, then the network structure is simple, but the heat map precision is insufficient
Solution Approach 1:
The patent implements local quality by applying differentiated learning intensities to different feature maps. Feature maps that contribute more significantly to key point detection undergo deeper learning processes with adjusted parameters, while less critical maps receive standard processing. This selective approach improves heat map precision by concentrating computational resources where they are most needed, rather than uniformly increasing complexity across all maps.
Solution Approach 2:
The system changes learning parameters such as learning rate and processing depth based on the specific characteristics and importance of each feature map. This parameter differentiation enables the network to achieve higher heat map precision by optimizing learning conditions for each map individually, avoiding the need for uniformly complex processing across the entire feature map set.
3Reliability
If the learning process is tailored to each feature map's importance, then detection accuracy improves, but the processing complexity increases
Solution Approach 1:
The patent achieves improved pose estimation reliability by applying tailored learning processes to feature maps based on their importance to key point detection. The network identifies which feature maps are most critical for accurate pose estimation and applies enhanced learning strategies specifically to those maps. This localized enhancement improves reliability without requiring the entire learning process to be complex, as only critical pathways receive intensified processing.
Data Source
AI summary
A method for detecting key points of skeleton, an apparatus, an electronic device, and a storage medium are provided. The method is implemented as follows. An original image is acquired. The original image includes a plurality of key points of skeleton. Based on a pre-trained stacked hourglass network structure, skeleton key point identification is performed on the original image to obtain heat maps of the plurality of key points. The stacked hourglass network structure includes at least one hourglass network. The at least one hourglass network is configured to perform deep-layer feature learning on feature maps of the plurality of key points based on weight values corresponding to the feature maps.