# Surveillance Video Parsing with Single Frame Supervision

arXiv: https://arxiv.org/abs/1611.09587 · HTML: https://mlsanity.com/paper/1611.09587/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2016.

## Built on by (most cited first, 13 of 13)

- [Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations](https://mlsanity.com/paper/2308.11796/) (2023)
- [Mask Propagation for Efficient Video Semantic Segmentation](https://mlsanity.com/paper/2310.18954/) (2023)
- [Mining Relations among Cross-Frame Affinities for Video Semantic Segmentation](https://mlsanity.com/paper/2207.10436/) (2022)
- [Learning Local and Global Temporal Contexts for Video Semantic Segmentation](https://mlsanity.com/paper/2204.03330/) (2022)
- [Domain Adaptive Video Segmentation via Temporal Consistency Regularization](https://mlsanity.com/paper/2107.11004/) (2021)
- [MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation](https://mlsanity.com/paper/2209.04471/) (2022)
- [Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation](https://mlsanity.com/paper/2309.08020/) (2023)
- [Towards Open-Vocabulary Video Semantic Segmentation](https://mlsanity.com/paper/2412.09329/) (2024)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [Efficient Video Segmentation Models with Per-frame Inference](https://mlsanity.com/paper/2202.12427/) (2022)
- [MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer](https://mlsanity.com/paper/2304.05930/) (2023)
- [Nondiscriminatory Treatment: a straightforward framework for multi-human parsing](https://mlsanity.com/paper/2101.10913/) (2021)
- [Can Unsupervised Segmentation Reduce Annotation Costs for Video Semantic Segmentation?](https://mlsanity.com/paper/2603.27697/) (2026)

## Abstract (arXiv)

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.