论文标题
使用两流剩余卷积网络的强大视觉对象跟踪
Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks
论文作者
论文摘要
当前的基于深度学习的视觉跟踪方法通过从离线模式下的大量监督培训数据中学习目标分类和/或估计模型非常成功。但是，由于一些更具挑战性的问题，例如密集的干扰物对象，背景，运动模糊等，它们仍然可能无法跟踪对象。受到人类“视觉跟踪”功能的启发，该功能利用运动提示将目标与背景区分开，我们提出了一个用于视觉跟踪的两流残留卷积网络（TS-RCN），该网络成功利用了模型更新的外观和运动功能。我们的TS-RCN可以与现有的基于深度学习的视觉跟踪器集成。为了进一步提高跟踪性能，我们采用“较宽”剩余网络Resnext作为其功能提取骨干。据我们所知，TS-RCN是第一个端到端的两流视觉跟踪系统，它充分利用了目标的外观和运动功能。我们已经在最广泛使用的基准数据集上广泛评估了TS-RCN，包括dot2018，dot2019和got-10k。实验结果成功地证明了我们的两流模型可以极大地胜过基于外观的跟踪器，并且还可以实现最先进的性能。跟踪系统最多可以运行38.1 fps。
The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised training data in offline mode. However, most of them can still fail in tracking objects due to some more challenging issues such as dense distractor objects, confusing background, motion blurs, and so on. Inspired by the human "visual tracking" capability which leverages motion cues to distinguish the target from the background, we propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking, which successfully exploits both appearance and motion features for model update. Our TS-RCN can be integrated with existing deep learning based visual trackers. To further improve the tracking performance, we adopt a "wider" residual network ResNeXt as its feature extraction backbone. To the best of our knowledge, TS-RCN is the first end-to-end trainable two-stream visual tracking system, which makes full use of both appearance and motion features of the target. We have extensively evaluated the TS-RCN on most widely used benchmark datasets including VOT2018, VOT2019, and GOT-10K. The experiment results have successfully demonstrated that our two-stream model can greatly outperform the appearance based tracker, and it also achieves state-of-the-art performance. The tracking system can run at up to 38.1 FPS.
