School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea.
Department of Computer Science, Information Technology University, Lahore 54000, Pakistan.
Sensors (Basel). 2020 Jul 20;20(14):4021. doi: 10.3390/s20144021.
We propose to improve the visual object tracking by introducing a soft mask based low-level feature fusion technique. The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. The proposed approach is integrated within a Siamese framework to demonstrate its effectiveness for visual object tracking. The proposed soft mask is used to give more importance to the target regions as compared to the other regions to enable effective target feature representation and to increase discriminative power. The low-level feature fusion improves the tracker robustness against distractors. The channel attention is used to identify more discriminative channels for better target representation. The spatial attention complements the soft mask based approach to better localize the target objects in challenging tracking scenarios. We evaluated our proposed approach over five publicly available benchmark datasets and performed extensive comparisons with 39 state-of-the-art tracking algorithms. The proposed tracker demonstrates excellent performance compared to the existing state-of-the-art trackers.
我们提出了一种基于软掩模的底层特征融合技术来改进视觉目标跟踪。该技术通过集成通道和空间注意力机制得到进一步加强。所提出的方法集成在一个孪生框架内，以证明其在视觉目标跟踪方面的有效性。所提出的软掩模用于与其他区域相比，给予目标区域更多的重要性，从而实现有效的目标特征表示并提高判别力。底层特征融合提高了跟踪器对干扰物的鲁棒性。通道注意力用于为更好的目标表示识别更具判别力的通道。空间注意力补充了基于软掩模的方法，以便在具有挑战性的跟踪场景中更好地定位目标对象。我们在五个公开可用的基准数据集上评估了我们提出的方法，并与 39 种最先进的跟踪算法进行了广泛的比较。与现有的最先进的跟踪器相比，所提出的跟踪器表现出了优异的性能。
