Fast Online Object Tracking and Segmentation: A Unifying Approach

Fast Online Object Tracking and Segmentation: A Unifying Approach

By Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H.S. Torr


This article was originally published on on arxiv.org on 12 Dec 2018.


Abstract

In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task. Once trained, SiamMask solely relies on a single bounding box initialisation and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second. Despite its simplicity, versatility and fast speed, our strategy allows us to establish a new state of the art among real-time trackers on VOT-2018, while at the same time demonstrating competitive performance and the best speed for the semi-supervised video object segmentation task on DAVIS-2016 and DAVIS-2017. The project website is this http URL.



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