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Abstract
Personnel behavior understanding under complex scenarios is a challenging task for computer vision. This paper proposes a novel Compact model, which we refer to as CGARPN that incorporates with Global Association relevance and Adaptive Routing Pose estimation Network. Our framework firstly introduces CGAN backbone to facilitate the feature representation by compressing the kernel parameter space compared with typical algorithms, effectively lowering the calculation capacity and consumption. The framework integrates the Global Association information between keypoints, and learns the correlation between high-dimensional feature parameters. ARPN introduced by our structure is established to sufficiently excavate the resembling properties of outcome concealed in the network, adaptively achieving remarkable performance by selecting compatible paths for optimization. Meanwhile, Parametric Content Similarity NMS (PCSNMS) is developed where detailed information on proposal boxes is associated. Comparative experiments (datasets on FLIC, MPII, etc.) with CNN-based counterparts have empirically demonstrated the effectiveness and competitiveness of the model in perspective of accuracy, memory consumption, and computation perplexity. Our model contributes to an efficient and feasible framework of human behavior apprehension.
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Authors (8)
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Lei Shi
- Chengdu University, China
-
Yimin Zhou
- Chengdu University, China
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Juan Wang
- Chengdu University, China
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Zuli Wang
- Chengdu University, China
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Ding Chen
- Chengdu University, China
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Haifeng Zhao
- Chengdu University, China
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Wankou Yang
- Chengdu University, China
Cite as
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Future Generation Computer Systems-The International Journal of Grid Computing-Theory Methods and Applications
no. 141,
pages 514 - 525,
ISSN: 0167-739X - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Shi L., Zhou Y., Wang J., Wang Z., Chen D., Zhao H., Yang W., Szczerbicki E.: Compact global association based adaptive routing framework for personnel behavior understanding// Future Generation Computer Systems-The International Journal of Grid Computing-Theory Methods and Applications -Vol. 141, (2023), s.514-525
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.future.2022.12.002
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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