CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition Article Swipe
Huanzhang Dou
,
Pengyi Zhang
,
Yuhan Zhao
,
Lu Jin
,
Xi Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.03632
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.03632
Gait recognition, which aims at identifying individuals by their walking patterns, has achieved great success based on silhouette. The binary silhouette sequence encodes the walking pattern within the sparse boundary representation. Therefore, most pixels in the silhouette are under-sensitive to the walking pattern since the sparse boundary lacks dense spatial-temporal information, which is suitable to be represented with dense texture. To enhance the sensitivity to the walking pattern while maintaining the robustness of recognition, we present a Complementary Learning with neural Architecture Search (CLASH) framework, consisting of walking pattern sensitive gait descriptor named dense spatial-temporal field (DSTF) and neural architecture search based complementary learning (NCL). Specifically, DSTF transforms the representation from the sparse binary boundary into the dense distance-based texture, which is sensitive to the walking pattern at the pixel level. Further, NCL presents a task-specific search space for complementary learning, which mutually complements the sensitivity of DSTF and the robustness of the silhouette to represent the walking pattern effectively. Extensive experiments demonstrate the effectiveness of the proposed methods under both in-the-lab and in-the-wild scenarios. On CASIA-B, we achieve rank-1 accuracy of 98.8%, 96.5%, and 89.3% under three conditions. On OU-MVLP, we achieve rank-1 accuracy of 91.9%. Under the latest in-the-wild datasets, we outperform the latest silhouette-based methods by 16.3% and 19.7% on Gait3D and GREW, respectively.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.03632
- https://arxiv.org/pdf/2407.03632
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400433893
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https://openalex.org/W4400433893Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.03632Digital Object Identifier
- Title
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CLASH: Complementary Learning with Neural Architecture Search for Gait RecognitionWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-07-04Full publication date if available
- Authors
-
Huanzhang Dou, Pengyi Zhang, Yuhan Zhao, Lu Jin, Xi LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.03632Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.03632Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.03632Direct OA link when available
- Concepts
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Gait, Architecture, Computer science, Artificial intelligence, Physical medicine and rehabilitation, Geography, Medicine, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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