Fast Online Object Tracking and Segmentation: A Unifying Approach

CVPR 2019 Qiang WangLi ZhangLuca BertinettoWeiming HuPhilip H. S. Torr

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... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Visual Object Tracking VOT2017/18 SiamMask Expected Average Overlap (EAO) 0.380 # 3