State-Aware Tracker for Real-Time Video Object Segmentation
Xi Chen,  Zuoxin Li,  Ye Yuan,  Gang Yu,  Jianxin Shen,  Donglian Qi

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 DAVIS 2017-Val dataset, which shows a decent trade-off between efficiency and accuracy.