Kinect can be utilized to capture motion data in real time. Given that its cost is lower than that of traditional motion-capture devices

Kinect is widely used to capture motion data. However

the noise in Kinect-captured motion data makes the quality of motion data relatively unsatisfactory. Thus

previous data-processing methods failed to handle such data well. Foot plant detection is a key procedure in motion editing; it detects whether the character's foot is on the ground. A robust foot plant detection algorithm for Kinect-captured motion data is proposed in this study. First

an adaptively bilateral filtering method is proposed to reduce the noise in Kinect-captured motion data. Second

multiple features of the motion data are defined and utilized to optimize the effect of foot plant detection. Finally

a decision function is trained with the support vector machine algorithm and applied to foot plant detection. After being applied to a dataset that consists of various types of motion

the noise in the Kinect-captured motion data was reduced effectively.The accuracy of foot plant detection increased by 6% after applying the proposed adaptively bilateral filtering method.Good time performance and high accuracy of foot plant detection were acquired as well. The foot plant detection accuracy of the proposed detection algorithm increased by 11% and 8% compared with that of the baseline method and the K nearest-neighbor method

respectively. The time consumed in the detection of the motion data of one frame is a seventh of that of the K nearest-neighbor method. Experimental results proved the effectiveness and robustness of the proposed foot plant detection algorithm. Thus

this algorithm can be widely utilized in motion data processing.