We present a new action classification method for skeletal sequence data. The proposed method is based on simple nonparametric feature matching without a learning process. We first augment the training dataset to implicitly construct an exponentially increasing number of training sequences, which can be used to improve the generalization power of the proposed action classifier. These augmented training sequences are matched to the test sequence with the relaxed dynamic time warping (DTW) technique. Our relaxed formulation allows the proposed method to work faster and with higher efficiency than the conventional DTW-based method using a non-augmented dataset. Experimental results show that the proposed approach produces effective action classification results for various scales of real datasets.

The present study considers an action-based person identification problem, in which an input action sequence consists of 3D skeletal data from multiple frames. Unlike previous approaches, the type of action is not pre-defined in this work, which requires the subject classifier to possess cross-action generalization capabilities. To achieve that, we present a novel pose-based Hough forest framework, in which each per-frame pose feature casts a probabilistic vote to the Hough space. Pose distribution is estimated from training data and then used to compute the reliability of the vote to deal with the unseen poses in the test action sequence. Experimental results with various real datasets demonstrate that the proposed method provides effective person identification results especially for the challenging cross-action person identification setting.