Hierarchical Aligned Cluster Analysis (HACA) for Temporal Clustering of Human Motion

Abstract

Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the variability in the temporal scale of human actions, the complexity of representing articulated motion, and the exponential nature of all possible movement combinations. We pose the problem of learning motion primitives as a temporal clustering one, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multi-dimensional time series into $m$ disjoint segments, such that each segment belongs to one of $k$ clusters. HACA combines kernel $k$-means with the generalized dynamic time alignment kernel to cluster time series. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available at http://humansensing.cs.cmu.edu/aca.