3D Gaussian bin based sparse representation for action recognition

We propose a new representation 3DGBOJ to quickly and precisely classify human action from a series of depth maps. We use Shotton et al's method to predict the best candidate of 3D skeletal joint locations from Kinect depth map. By normalizing and retargeting the human skeleton to a common skeleton, we eliminate the noisy introduced by human agent diversity and view dependent. Some impossible motions are deleted with regard to Kinematics constraint. We design a 3D Gaussian space to map each joint to a bin based sparse feature vector. To weaken the timescale variation, which occurs during the performance with different speed and style, we remove the consecutive repeated vectors. We cluster the motion feature vectors with Affinity Propagation and treat each motion exemplar as a vocabulary in bag of feature (BOF). To better handle overlapping features and contextual dependencies, we trained them over a linear CRFs model. The experiment result shows that our representation maintains appropriate adaptability to variations of different subjects of different gender and size, and with different speed and style from different view.