Fast Online Object Tracking and Segmentation: A Unifying Approach
| Autor: | Qiang Wang, Luca Bertinetto, Philip H. S. Torr, Weiming Hu, Li Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Deep learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology Image segmentation Frame rate Object (computer science) Object detection Minimum bounding box Video tracking 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence business |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.1812.05050 |
| Popis: | 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 http://www.robots.ox.ac.uk/~qwang/SiamMask. Comment: CVPR 2019 camera ready. Code available at https://github.com/foolwood/SiamMask |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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