UWA 3D Multiview Activity II Dataset

This dataset was collected in our lab using Kinect to emphasize three points: (1) Larger number of human activities. (2) Each subject performed all actions in a continuous manner with no breaks or pauses. Therefore, the start and end positions of body for the same actions are different. (3) Each subject performed the same actions four times while imaged from four different views: front view, left and right side views, and top view.This dataset consists of 30 human activities performed by 10 subjects with different scales: (1) one hand waving, (2) one hand Punching, (3) two hand waving , (4) two hand punching, (5) sitting down, (6) standing up, (7) vibrating, (8) falling down, (9) holding chest, (10) holding head, (11) holding back, (12) walking, (13) irregular walking, (14) lying down, (15) turning around, (16) drinking , (17) phone answering, (18) bending, (19) jumping jack, (20) running, (21) picking up, (22) putting down, (23) kicking, (24) jumping, (25) dancing, (26) moping floor, (27) sneezing, (28) sitting down (chair), (29) squatting, and (30) coughing. To capture depth videos, each subject performed 30 activities 4 times in a continuous manner. Each time, the Kinect was moved to a different angle to capture the actions from four different views. Note that this approach generates more challenging data than when actions are captured simultaneously from different viewpoints. We organized our dataset by segmenting the continuous sequences of activities. The dataset is challenging because of varying viewpoints, self-occlusion and high similarity among activities. For example, the actions (16) drinking and (17) phone answering have very similar motion, but the location of hand in these two actions is slightly different. Also, some actions such as (10) holding head and (11) holding back, have self-occlusion. Moreover, in the top view, the lower part of the body was not properly captured because of occlusion.

本数据集由本实验室使用Kinect采集，旨在突出三大核心设计要点：(1) 覆盖更丰富的人类动作类别；(2) 每位受试者需以无中断的连续方式完成所有动作，因此同一动作的身体起始与终止位置存在差异；(3) 每位受试者需完成4轮相同动作的录制，且从四种不同视角采集：正视图、左侧视图、右侧视图以及俯视图。

本数据集包含10名受试者完成的30类人类动作，涵盖不同动作尺度：(1) 单手挥动；(2) 单手出拳；(3) 双手挥动；(4) 双手出拳；(5) 坐下；(6) 站起；(7) 肢体震颤；(8) 摔倒；(9) 抚胸；(10) 扶头；(11) 扶背；(12) 正常行走；(13) 不规则行走；(14) 躺卧；(15) 转身；(16) 饮水；(17) 接打电话；(18) 弯腰；(19) 开合跳；(20) 跑步；(21) 拾取；(22) 放置；(23) 踢击；(24) 跳跃；(25) 舞蹈；(26) 拖地；(27) 打喷嚏；(28) 坐椅落座；(29) 下蹲；(30) 咳嗽。

为采集深度视频，每位受试者需以连续无间断的方式完成4轮30类动作的录制。每一轮录制时，均调整Kinect至不同角度，以从四种视角捕捉动作。需注意，相较于从多视角同时采集动作的方案，本采集方式生成的数据更具挑战性。

本数据集通过对连续动作序列进行分段实现组织。该数据集的挑战性体现在多视角变化、自身遮挡以及动作间高相似性。例如，(16)饮水与(17)接打电话两类动作的运动轨迹高度相似，仅手部位置存在细微差异。此外，部分动作如(10)扶头与(11)扶背存在自身遮挡问题。再者，在俯视图视角下，由于遮挡原因，人体下肢未能被完整采集。

搜集汇总

背景与挑战

背景概述

UWA 3D Multiview Activity II Dataset是一个多视角人类活动识别数据集，包含30种活动、10名受试者，通过Kinect从四个不同视角采集深度、RGB和骨架数据，具有连续动作执行和多视角挑战性。