  We present an effective dynamic clustering algorithm for the task of temporal
human action segmentation, which has comprehensive applications such as
robotics, motion analysis, and patient monitoring. Our proposed algorithm is
unsupervised, fast, generic to process various types of features, and
applicable in both the online and offline settings. We perform extensive
experiments of processing data streams, and show that our algorithm achieves
the state-of-the-art results for both online and offline settings.
