Academy of Astronautics, Northwestern Polytechnical University, YouYi Street, Xi'an 710072, China.
Sensors (Basel). 2018 Jul 20;18(7):2359. doi: 10.3390/s18072359.
Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. The existing spatially regularized discriminative correlation filter (SRDCF) method learns partial-target information or background information when experiencing rotation, out of view, and heavy occlusion. In order to reduce the computational complexity by creating a novel method to enhance tracking ability, we first introduce an adaptive dimensionality reduction technique to extract the features from the image, based on pre-trained VGG-Net. We then propose an adaptive model update to assign weights during an update procedure depending on the peak-to-sidelobe ratio. Finally, we combine the online SRDCF-based tracker with the offline Siamese tracker to accomplish long term tracking. Experimental results demonstrate that the proposed tracker has satisfactory performance in a wide range of challenging tracking scenarios.
鲁棒且精确的视觉跟踪是计算机视觉领域最具挑战性的问题之一。由于训练数据的固有缺乏，构建目标外观模型的稳健方法至关重要。现有的空间正则化判别相关滤波器 (SRDCF) 方法在经历旋转、不可见和严重遮挡时会学习到部分目标信息或背景信息。为了降低计算复杂度并提出一种新的方法来增强跟踪能力，我们首先引入了一种自适应降维技术，基于预训练的 VGG-Net 从图像中提取特征。然后，我们提出了一种自适应模型更新方法，在更新过程中根据峰值与旁瓣比来分配权重。最后，我们将基于在线 SRDCF 的跟踪器与离线的孪生跟踪器相结合，以实现长期跟踪。实验结果表明，所提出的跟踪器在广泛的挑战性跟踪场景中具有令人满意的性能。
