WEAR：一个用于可穿戴与自我中心活动识别的户外运动数据集
WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition
AI总结：
本文提出WEAR数据集，包含22名参与者在11个户外地点的18种锻炼活动的同步惯性与自我中心视频数据，验证了单阶段TAL模型在视觉与惯性数据融合上的有效性。
摘要：
研究表明，基于相机和基于惯性的数据在建模人类活动时具有互补性，然而同时包含自我中心视频和基于惯性传感器数据的数据集仍然稀缺。在本文中，我们介绍了WEAR，一个用于基于视觉和基于惯性的人类活动识别（HAR）的户外运动数据集。收集了22名参与者在11个不同户外地点执行共18种不同锻炼活动的数据，记录了同步的惯性（加速度）和相机（自我中心视频）数据。WEAR提供了一个在变化的户外环境中使用符合真实应用最新趋势的传感器放置方式的具有挑战性的预测场景。基准结果表明，通过我们的传感器放置方式，每种模态有趣地在预测性能上提供了互补的优势和劣势。此外，鉴于单阶段时序动作定位（TAL）模型近期的成功，我们展示了其多功能性：不仅能使用视觉数据进行训练，还能使用原始惯性数据，并能够通过简单的拼接融合这两种模态。用于复现实验的数据集和代码公开于：mariusbock.github.io/wear/。
英文摘要：
Research has shown the complementarity of camera- and inertial-based data for modeling human activities, yet datasets with both egocentric video and inertial-based sensor data remain scarce. In this paper, we introduce WEAR, an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). Data from 22 participants performing a total of 18 different workout activities was collected with synchronized inertial (acceleration) and camera (egocentric video) data recorded at 11 different outside locations. WEAR provides a challenging prediction scenario in changing outdoor environments using a sensor placement, in line with recent trends in real-world applications. Benchmark results show that through our sensor placement, each modality interestingly offers complementary strengths and weaknesses in their prediction performance. Further, in light of the recent success of single-stage Temporal Action Localization (TAL) models, we demonstrate their versatility of not only being trained using visual data, but also using raw inertial data and being capable to fuse both modalities by means of simple concatenation. The dataset and code to reproduce experiments is publicly available via: mariusbock.github.io/wear/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
