Unsupervised method for video action segmentation through spatio-temporal and positional-encoded embeddings.

Action segmentation consists of temporally segmenting a video and labeling each segmented interval with a specific action label. In this work, we propose a novel action segmentation method that requires no prior video analysis and no annotated data. Our method involves extracting spatio-temporal features from videos using a pre-trained deep network. Data is then transformed using a positional encoder, and finally a clustering algorithm is applied, where each produced cluster presumably corresponds to a different single and distinguishable action. In experiments, we show that our method produces competitive results on the Breakfast and Inria Instructional Videos dataset benchmarks.