论文标题
实时视频对象细分的州感知跟踪器
State-Aware Tracker for Real-Time Video Object Segmentation
论文作者
论文摘要
在这项工作中，我们解决了半监督视频对象细分（VOS）的任务，并探索如何有效利用视频属性来应对半居所的挑战。我们提出了一种称为州感知跟踪器（SAT）的新型管道，该管道可以以实时速度产生准确的分割结果。为了提高效率，SAT利用了框架间的一致性，并将每个目标对象作为曲目对象进行处理。为了在视频序列上进行更稳定，更稳定的性能，SAT可以对每个状态都有意识，并通过两个反馈循环自我适应。一个循环有助于生成更稳定的曲目。另一个循环有助于构建更强大和更全面的目标表示。 SAT在Davis2017-VAL数据集中取得了72.3％的J＆F的有希望的结果，这表明效率和准确性之间的权衡不错。代码将在github.com/megviidetection/video_analyst上发布。
In this work, we address the task of semi-supervised video object segmentation(VOS) and explore how to make efficient use of video property to tackle the challenge of semi-supervision. We propose a novel pipeline called State-Aware Tracker(SAT), which can produce accurate segmentation results with real-time speed. For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet. For more stable and robust performance over video sequences, SAT gets awareness for each state and makes self-adaptation via two feedback loops. One loop assists SAT in generating more stable tracklets. The other loop helps to construct a more robust and holistic target representation. SAT achieves a promising result of 72.3% J&F mean with 39 FPS on DAVIS2017-Val dataset, which shows a decent trade-off between efficiency and accuracy. Code will be released at github.com/MegviiDetection/video_analyst.
