Surveillance Video Parsing With Single Frame Supervision

Si Liu, Changhu Wang, Ruihe Qian, Han Yu, Renda Bao, Yao Sun; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 413-421

Surveillance video parsing, which segments the video frames into several labels, i.e., face, pants, left-leg, has wide applications. However, annotating all frames pixel-wisely 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 reveal that SVP is superior than state-of-the-arts.