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Fast Online Object Tracking and Segmentation: A Unifying Approach

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

* means equal contribution

 
pipeline picture
 

In this paper we illustrate how to perform both realtime object tracking and semi-supervised video object segmentation 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 the 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 35 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.

 

▸ Paper  (arXiv)

 

▸ Code  

 

▸ Results (v1)   [ VOT2016 (v1)] [ VOT2018 (v1)] [ DAVIS2016 (v1)] [ DAVIS2017 (v1)] [ DAVIS2017 test-dev (v1)] [ Youtube-VOS val (v1)]

 

▸ bibtex 

@article{Wang2018SiamMask,
    title={Fast Online Object Tracking and Segmentation: A Unifying Approach},
    author={Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip HS},
    journal={arXiv preprint arXiv:1812.05050},
    year={2018}
}

Example videos

demo picture