  We propose an action parsing algorithm to parse a video sequence containing
an unknown number of actions into its action segments. We argue that context
information, particularly the temporal information about other actions in the
video sequence, is valuable for action segmentation. The proposed parsing
algorithm temporally segments the video sequence into action segments. The
optimal temporal segmentation is found using a dynamic programming search
algorithm that optimizes the overall classification confidence score. The
classification score of each segment is determined using local features
calculated from that segment as well as context features calculated from other
candidate action segments of the sequence. Experimental results on the
Breakfast activity data-set showed improved segmentation accuracy compared to
existing state-of-the-art parsing techniques.
