Elderly Activity Recognition in the Wild: Results from the EAR Challenge
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917950208868352 |
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| author | Duong, Anh-Kiet |
| author_facet | Duong, Anh-Kiet |
| contents | This paper presents our solution for the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls Workshop at WACV 2025. The competition focuses on recognizing Activities of Daily Living (ADLs) performed by the elderly, covering six action categories with a diverse dataset. Our approach builds upon a state-of-the-art action recognition model, fine-tuned through transfer learning on elderly-specific datasets to enhance adaptability. To improve generalization and mitigate dataset bias, we carefully curated training data from multiple publicly available sources and applied targeted pre-processing techniques. Our solution currently achieves 0.81455 accuracy on the public leaderboard, highlighting its effectiveness in classifying elderly activities. Source codes are publicly available at https://github.com/ffyyytt/EAR-WACV25-DAKiet-TSM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07821 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Elderly Activity Recognition in the Wild: Results from the EAR Challenge Duong, Anh-Kiet Computer Vision and Pattern Recognition This paper presents our solution for the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls Workshop at WACV 2025. The competition focuses on recognizing Activities of Daily Living (ADLs) performed by the elderly, covering six action categories with a diverse dataset. Our approach builds upon a state-of-the-art action recognition model, fine-tuned through transfer learning on elderly-specific datasets to enhance adaptability. To improve generalization and mitigate dataset bias, we carefully curated training data from multiple publicly available sources and applied targeted pre-processing techniques. Our solution currently achieves 0.81455 accuracy on the public leaderboard, highlighting its effectiveness in classifying elderly activities. Source codes are publicly available at https://github.com/ffyyytt/EAR-WACV25-DAKiet-TSM. |
| title | Elderly Activity Recognition in the Wild: Results from the EAR Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07821 |