State-Aware Tracker for Real-Time Video Object Segmentation

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...

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Bibliographic Details
Main Authors: Chen, Xi, Li, Zuoxin, Yuan, Ye, Yu, Gang, Shen, Jianxin, Qi, Donglian
Format: Conference Proceeding
Language:English
Subjects:
Object segmentation
Pipelines
Robustness
State estimation
Target tracking
Task analysis
Video sequences
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Summary: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.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.00940