Abstract: A method for motion prediction is provided. The method includes: capturing, by a single camera, a plurality of cameras or 3D software, images of an object at multiple angles to generate multi-view images of the object; synthesizing motion capture (MoCap) data according to the multi-view images; projecting a masking object onto the object to generate multi-view training images, wherein the multi-view training images are images in which parts of limbs of the object are unoccluded and other parts of the limbs of the object are occluded; and using the motion capture data and the multi-view training images to train a predictive model.

Abstract: A motion tracking system includes a first image-capturing module, a computing module and a database. The first image-capturing module captures the full body motion of an object to obtain a depth image. The database provides a plurality of training samples, wherein the training samples include a plurality of depth feature information related to joint positions of the object. The computing module receives the depth image, performs an association operation and a prediction operation for the depth image to obtain a plurality of first joint positions of the object. The computing module projects the first joint positions to a three-dimensional space to generate a three-dimensional skeleton of the object. The depth image includes an image in which limbs of the object are not occluded or an image in which some of limbs of the object are not occluded and the others of limbs of the object are occluded.

Abstract: A method for motion prediction is provided. The method includes: capturing, by a single camera, a plurality of cameras or 3D software, images of an object at multiple angles to generate multi-view images of the object; synthesizing motion capture (MoCap) data according to the multi-view images; projecting a masking object onto the object to generate multi-view training images, wherein the multi-view training images are images in which parts of limbs of the object are unoccluded and other parts of the limbs of the object are occluded; and using the motion capture data and the multi-view training images to train a predictive model.

Abstract: A motion tracking system includes a first image-capturing module, a computing module and a database. The first image-capturing module captures the full body motion of an object to obtain a depth image. The database provides a plurality of training samples, wherein the training samples include a plurality of depth feature information related to joint positions of the object. The computing module receives the depth image, performs an association operation and a prediction operation for the depth image to obtain a plurality of first joint positions of the object. The computing module projects the first joint positions to a three-dimensional space to generate a three-dimensional skeleton of the object. The depth image includes an image in which limbs of the object are not occluded or an image in which some of limbs of the object are not occluded and the others of limbs of the object are occluded.

Abstract: A tapping detecting device, a tapping detecting method and a smart projecting system using the same are provided. The tapping detecting device comprises a fingertip size providing unit, a laser emitting unit, a laser sensing unit and a processing unit. The fingertip size providing unit is used for providing the size of a fingertip of a finger. The laser emitting unit is used for providing a laser at a parallel surface above a plane. The laser sensing unit is used for capturing a reflecting image which shows the reflection of the laser caused by the fingertip. The processing unit is used for obtaining a reflecting region according to the reflecting image, and for adjusting a threshold value according to the size of the fingertip. If the area of the reflecting region is larger than the threshold value, then the processing unit deems that the fingertip touches the plane.