Fast Online Object Tracking and Segmentation: A Unifying Approach

12 Dec 2018Qiang Wang • Li Zhang • Luca Bertinetto • Weiming Hu • Philip 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. 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.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Visual Object Tracking VOT2017/18 SiamMask Expected Average Overlap (EAO) 0.380 # 1