# Video Object Segmentation using Tracked Object Proposals

arXiv: https://arxiv.org/abs/1707.06545 · HTML: https://mlsanity.com/paper/1707.06545/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [RGB-D Video Object Segmentation via Enhanced Multi-store Feature Memory](https://mlsanity.com/paper/2504.16471/) (2025)
- [Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation](https://mlsanity.com/paper/2106.03330/) (2021)

## Abstract (arXiv)

We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 challenge. Our approach combines category-based object detection, category-independent object appearance segmentation and temporal object tracking. We are motivated by the fact that the objects semantic category tends not to change throughout the video while its appearance and location can vary considerably. In order to capture the specific object appearance independent of its category, for each video we train a fully convolutional network using augmentations of the given annotated frame. We refine the appearance segmentation mask with the bounding boxes provided either by a semantic object detection network, when applicable, or by a previous frame prediction. By introducing a temporal continuity constraint on the detected boxes, we are able to improve the object segmentation mask of the appearance network and achieve competitive results on the DAVIS datasets.