Data augmented dynamic time warping for skeletal action classification - Chang, Ju Yong; Heo, Yong Seok (IEICE Transactions on Information and Systems, 2018)
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.
Keywords: Action classifications; Action classifier; Data augmentation; Dynamic time warping; Higher efficiency; Learning process; Training dataset; Training sequences; Action classification; Data augmentation; Dynamic time warping