SiamMask: A Framework for Fast Online Object Tracking and Segmentation

• URL: http://arxiv.org/abs/2207.02088v1
• Date: Tue, 5 Jul 2022 14:47:17 GMT
• Title: SiamMask: A Framework for Fast Online Object Tracking and Segmentation
• Authors: Weiming Hu, Qiang Wang, Li Zhang, Luca Bertinetto, Philip H.S. Torr
• Abstract summary: SiamMask is a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method. We show that it is possible to extend the framework to handle multiple object tracking and segmentation by simply re-using the multi-task model. It yields real-time state-of-the-art results on visual-object tracking benchmarks, while at the same time demonstrating competitive performance at a high speed for video object segmentation benchmarks.
• Score: 96.61632757952292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method. We improve the offline training procedure of popular fully-convolutional Siamese approaches by augmenting their losses with a binary segmentation task. Once the offline training is completed, SiamMask only requires a single bounding box for initialization and can simultaneously carry out visual object tracking and segmentation at high frame-rates. Moreover, we show that it is possible to extend the framework to handle multiple object tracking and segmentation by simply re-using the multi-task model in a cascaded fashion. Experimental results show that our approach has high processing efficiency, at around 55 frames per second. It yields real-time state-of-the-art results on visual-object tracking benchmarks, while at the same time demonstrating competitive performance at a high speed for video object segmentation benchmarks.

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