School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China.
Neural Netw. 2021 Aug;140:344-354. doi: 10.1016/j.neunet.2021.04.004. Epub 2021 Apr 16.
Existing trackers usually exploit robust features or online updating mechanisms to deal with target variations which is a key challenge in visual tracking. However, the features being robust to variations remain little spatial information, and existing online updating methods are prone to overfitting. In this paper, we propose a dual-margin model for robust and accurate visual tracking. The dual-margin model comprises an intra-object margin between different target appearances and an inter-object margin between the target and the background. The proposed method is able to not only distinguish the target from the background but also perceive the target changes, which tracks target appearance changing and facilitates accurate target state estimation. In addition, to exploit rich off-line video data and learn general rules of target appearance variations, we train the dual-margin model on a large off-line video dataset. We perform tracking under a Siamese framework using the constructed appearance set as templates. The proposed method achieves accurate and robust tracking performance on five public datasets while running in real-time. The favorable performance against the state-of-the-art methods demonstrates the effectiveness of the proposed algorithm.
现有的跟踪器通常利用鲁棒的特征或在线更新机制来处理目标变化，这是视觉跟踪的一个关键挑战。然而，对变化具有鲁棒性的特征仍然保留很少的空间信息，并且现有的在线更新方法容易出现过拟合。在本文中，我们提出了一种用于鲁棒和准确视觉跟踪的双边缘模型。双边缘模型包括不同目标外观之间的内部目标边缘和目标与背景之间的外部目标边缘。所提出的方法不仅能够区分目标和背景，还能够感知目标变化，从而跟踪目标外观变化并促进准确的目标状态估计。此外，为了利用丰富的离线视频数据并学习目标外观变化的一般规则，我们在大型离线视频数据集上训练双边缘模型。我们使用构建的外观集作为模板在 Siamese 框架下进行跟踪。所提出的方法在五个公共数据集上实现了准确和鲁棒的跟踪性能，同时实时运行。与最先进的方法相比，所提出的算法的良好性能证明了其有效性。
