Chen Hang, Zhang Weiguo, Yan Danghui
Automation College, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel). 2021 Jan 28;21(3):889. doi: 10.3390/s21030889.
Object information significantly affects the performance of visual tracking. However, it is difficult to obtain accurate target foreground information because of the existence of challenging scenarios, such as occlusion, background clutter, drastic change of appearance, and so forth. Traditional correlation filter methods roughly use linear interpolation to update the model, which may lead to the introduction of noise and the loss of reliable target information, resulting in the degradation of tracking performance. In this paper, we propose a novel robust visual tracking framework with reliable object information and Kalman filter (KF). Firstly, we analyze the reliability of the tracking process, calculate the confidence of the target information at the current estimated location, and determine whether it is necessary to carry out the online training and update step. Secondly, we also model the target motion between frames with a KF module, and use it to supplement the correlation filter estimation. Finally, in order to keep the most reliable target information of the first frame in the whole tracking process, we propose a new online training method, which can improve the robustness of the tracker. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of our proposed method, and our method achieves a comparable or better performance compared with several other state-of-the-art trackers.
目标信息对视觉跟踪性能有显著影响。然而，由于存在诸如遮挡、背景杂乱、外观急剧变化等具有挑战性的场景，难以获得准确的目标前景信息。传统的相关滤波器方法大致使用线性插值来更新模型，这可能导致噪声的引入和可靠目标信息的丢失，从而导致跟踪性能下降。在本文中，我们提出了一种具有可靠目标信息和卡尔曼滤波器（KF）的新型鲁棒视觉跟踪框架。首先，我们分析跟踪过程的可靠性，计算当前估计位置处目标信息的置信度，并确定是否有必要执行在线训练和更新步骤。其次，我们还使用KF模块对帧间目标运动进行建模，并利用它来补充相关滤波器估计。最后，为了在整个跟踪过程中保留第一帧中最可靠的目标信息，我们提出了一种新的在线训练方法，该方法可以提高跟踪器的鲁棒性。在多个基准上进行的大量实验证明了我们提出的方法的有效性和鲁棒性，并且我们的方法与其他几种先进跟踪器相比实现了相当或更好的性能。
