Surveillance Video Parsing with Single Frame Supervision

2016/11/29 by Si Liu, Changhu Wang, Liu, Si +7 · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.1611.09587

openalex publication_date 2016/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Joint Object and Part Segmentation using Deep Learned Potentials
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Semantic Image Segmentation via Deep Parsing Network
• Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
• BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
• What's the Point: Semantic Segmentation with Point Supervision
• Caffe: Convolutional Architecture for Fast Feature Embedding
• FlowNet: Learning Optical Flow with Convolutional Networks
• Optical Flow with Semantic Segmentation and Localized Layers
• Fully Convolutional Networks for Semantic Segmentation
• Learning Deconvolution Network for Semantic Segmentation
• Flowing ConvNets for Human Pose Estimation in Videos

Cited by

• Hierarchical Human Parsing with Typed Part-Relation Reasoning

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