  Surveillance video parsing, which segments the video frames into several
labels, e.g., face, pants, left-leg, has wide applications.
However,pixel-wisely annotating all frames is tedious and inefficient. In this
paper, we develop a Single frame Video Parsing (SVP) method which requires only
one labeled frame per video in training stage. To parse one particular frame,
the video segment preceding the frame is jointly considered. SVP (1) roughly
parses the frames within the video segment, (2) estimates the optical flow
between frames and (3) fuses the rough parsing results warped by optical flow
to produce the refined parsing result. The three components of SVP, namely
frame parsing, optical flow estimation and temporal fusion are integrated in an
end-to-end manner. Experimental results on two surveillance video datasets show
the superiority of SVP over state-of-the-arts.
