Fast Online Object Tracking and Segmentation: A Unifying Approach

Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H.S. Torr; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 1328-1338

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.

Related Material


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[bibtex]
@InProceedings{Wang_2019_CVPR,
author = {Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip H.S.},
title = {Fast Online Object Tracking and Segmentation: A Unifying Approach},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}