Skeleton-based Temporal Segmentation of Human Activities from Video Sequences

This paper presents a new multi-step, skeleton-based approach for the temporal segmentation of human activities from video sequences. Several signals are first extracted from a skeleton sequence. These signals are then segmented individually to localize their cyclic segments. Finally, all individual segmentations are merged with respect to the global set of signals. Our approach requires no prior knowledge on human activities and can use any generic stick-model. Two different techniques for signal segmentation and for the fusion of the individual segmentations are proposed and tested on a database of fifteen video sequences of variable level of complexity.