Surveillance Video Parsing with Single Frame Supervision

Abstract

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.

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Cited by in corpus (5)

• Video Semantic Segmentation with Distortion-Aware Feature Correction
• PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
• Cross-domain Human Parsing via Adversarial Feature and Label Adaptation
• Hierarchical Human Parsing with Typed Part-Relation Reasoning
• Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel