针对老年人日常活动识别，提出高效适配的迁移学习方案。
Elderly Activity Recognition in the Wild: Results from the EAR Challenge
- 基于先进模型，用老年人数据微调提升适应性。
- 融合多源数据并预处理，准确率达0.81455。
- 适合智能养老、动作识别研究者参考。
本文介绍我们在WACV 2025计算机视觉助力小规模群体研讨会举办的老年人动作识别（EAR）挑战赛中的解决方案。比赛聚焦于识别老年人执行的日常生活活动（ADLs），涵盖六类动作，数据集具有多样性。我们的方法基于当前最先进的动作识别模型，通过在老年人专属数据集上进行迁移学习微调，增强模型适应性。为提升泛化能力并缓解数据偏差，我们从多个公开数据源精心筛选训练数据，并应用针对性预处理技术。当前方案在公共排行榜上取得0.81455的准确率，验证了其在识别老年人动作方面的有效性。源代码已公开于 https://github.com/ffyyytt/EAR-WACV25-DAKiet-TSM。
原文摘要 · Abstract (English)
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.
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