Human activity recognition using dynamic representation and matching of skeleton feature sequences from RGB-D images

Segmenting a skeleton feature sequence into pose and motion feature segments can improve the effectiveness of key pose based human activity recognition. Nevertheless, the fixed number of atomic motions in feature segments results in the lost of temporal information, it makes the segmentation technique hardly suitable for all scenarios of feature sequences. To address this issue, this paper proposes a human activity recognition method using dynamic representation and matching of skeleton feature sequences based on the segmentation technique. In our method, a skeleton feature sequence is first segmented into key pose and atomic motion segments according to the potential differences. Afterwards, a learning strategy is proposed to select some frame sets with high confidence value, and we then extract series of poses (i.e., atomic motion series) with different numbers of poses from the learnt frame sets to represent motion feature segments, while a fixed number of centroids obtained by K-Means are used to represent pose feature segments. Finally, the shape dynamic time warping (shapeDTW) algorithm is utilized to measure the distance between the corresponding motion feature segments of two feature sequences. Comprehensive experiments are conducted on three public human activity datasets, and the results show that our method achieves superior performances in comparison with some state-of-the-art methods.