Fully Convolutional Online Tracking

• URL: http://arxiv.org/abs/2004.07109v5
• Date: Sun, 26 Sep 2021 09:13:16 GMT
• Title: Fully Convolutional Online Tracking
• Authors: Yutao Cui, Cheng Jiang, Limin Wang, Gangshan Wu
• Abstract summary: We present a fully convolutional online tracking framework, coined as FCOT, for both classification and regression branches. Our key contribution is to introduce an online regression model generator (RMG) for initializing weights of the target filter with online samples. Thanks to the unique design of RMG, our FCOT can not only be more effective in handling target variation along temporal dimension thus generating more precise results.
• Score: 47.78513247048846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online learning has turned out to be effective for improving tracking performance. However, it could be simply applied for classification branch, but still remains challenging to adapt to regression branch due to its complex design and intrinsic requirement for high-quality online samples. To tackle this issue, we present the fully convolutional online tracking framework, coined as FCOT, and focus on enabling online learning for both classification and regression branches by using a target filter based tracking paradigm. Our key contribution is to introduce an online regression model generator (RMG) for initializing weights of the target filter with online samples and then optimizing this target filter weights based on the groundtruth samples at the first frame. Based on the online RGM, we devise a simple anchor-free tracker (FCOT), composed of a feature backbone, an up-sampling decoder, a multi-scale classification branch, and a multi-scale regression branch. Thanks to the unique design of RMG, our FCOT can not only be more effective in handling target variation along temporal dimension thus generating more precise results, but also overcome the issue of error accumulation during the tracking procedure. In addition, due to its simplicity in design, our FCOT could be trained and deployed in a fully convolutional manner with a real-time running speed. The proposed FCOT achieves the state-of-the-art performance on seven benchmarks, including VOT2018, LaSOT, TrackingNet, GOT-10k, OTB100, UAV123, and NFS. Code and models of our FCOT have been released at: \url{https://github.com/MCG-NJU/FCOT}.

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