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.