构建首个日常活动标注的自拍日志数据集，提升行为检索的准确性与可解释性。
LSC-ADL: An Activity of Daily Living (ADL)-Annotated Lifelog Dataset Generated via Semi-Automatic Clustering
- 采用半自动聚类结合人工校验，用HDBSCAN生成精准活动标签
- 在LSC数据集基础上新增ADL结构化语义层，支持细粒度检索
- 适合研究自拍视觉、行为识别与上下文感知检索的学者使用
自拍日志通过可穿戴相机持续记录个人数据，提供第一人称视角的日常活动视图。现有日志检索方法普遍缺乏活动级别标注，难以捕捉时间关联与语义信息。本文提出LSC-ADL，基于LSC数据集构建的日常活动（ADL）标注日志数据集，引入结构化语义层以增强理解。采用半自动方法，结合HDBSCAN进行类内聚类，并通过人机协同验证，生成高精度的ADL标注，显著提升检索的可解释性。将动作识别融入日志检索流程，弥补当前研究空白，实现更贴近真实场景的上下文感知表示。该数据集有望推动自拍视觉、行为识别与日志检索的研究进展，提升内容检索的准确率与可解释性。标注数据可从 https://bit.ly/lsc-adl-annotations 下载。
原文摘要 · Abstract (English)
Lifelogging involves continuously capturing personal data through wearable cameras, providing an egocentric view of daily activities. Lifelog retrieval aims to search and retrieve relevant moments from this data, yet existing methods largely overlook activity-level annotations, which capture temporal relationships and enrich semantic understanding. In this work, we introduce LSC-ADL, an ADL-annotated lifelog dataset derived from the LSC dataset, incorporating Activities of Daily Living (ADLs) as a structured semantic layer. Using a semi-automatic approach featuring the HDBSCAN algorithm for intra-class clustering and human-in-the-loop verification, we generate accurate ADL annotations to enhance retrieval explainability. By integrating action recognition into lifelog retrieval, LSC-ADL bridges a critical gap in existing research, offering a more context-aware representation of daily life. We believe this dataset will advance research in lifelog retrieval, activity recognition, and egocentric vision, ultimately improving the accuracy and interpretability of retrieved content. The ADL annotations can be downloaded at https://bit.ly/lsc-adl-annotations.
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