UWA 3D Multiview Activity II Dataset
收藏资源简介：
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，重点覆盖三大核心设计原则：(1) 涵盖更丰富的人体动作类别；(2) 每位受试者全程连续完成所有动作，无任何中断或停顿，因此同一动作的人体起始与结束姿态存在显著差异；(3) 每位受试者在四种不同视角下各完成四次同一动作，分别为正面视角、左右侧面视角与顶面视角。 本数据集包含10名不同体型的受试者完成的30类人体动作，具体类别如下：(1) 单臂挥手，(2) 单臂出拳，(3) 双臂挥手，(4) 双臂出拳，(5) 坐下，(6) 站起，(7) 肢体震颤，(8) 摔倒，(9) 按抚胸部，(10) 按抚头部，(11) 按抚背部，(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) 拾取物品，(22) 放置物品，(23) 踢击，(24) 跳跃，(25) 舞蹈，(26) 拖地，(27) 打喷嚏，(28) 坐椅子，(29) 下蹲，(30) 咳嗽。 为采集深度视频，每位受试者以连续无间断的方式完成全部30类动作各4次。每次采集时，调整Kinect至不同角度，从四种预设视角捕捉动作。需注意，该采集方式相较于同时从多视角同步采集动作，生成的数据更具挑战性。 我们通过对连续动作序列进行分段处理来构建本数据集。该数据集存在诸多挑战性因素：视角多变、存在自身遮挡现象，且部分动作间相似度较高。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；又如(10)按抚头部与(11)按抚背部等动作存在明显的自身遮挡问题。此外，在顶面视角下，由于遮挡效应，人体下半身无法被完整采集。
