噪声环境下ECO与CFNet跟踪器的比较研究
Comparative Study of ECO and CFNet Trackers in Noisy Environment
摘要：
目标跟踪是最具挑战性的任务之一，在过去的二十年里得到了计算机视觉研究人员的极大关注。最近基于深度学习的跟踪器在各种跟踪挑战中表现出良好的性能。跟踪方法应该在诸如变形、低分辨率、遮挡、尺度和光线变化的挑战中准确地跟踪连续帧中的对象。大多数跟踪器在特定的挑战上实现了良好的性能，而不是所有的跟踪问题，因此缺乏可以在所有条件下都表现良好的通用跟踪算法。此外，跟踪技术的性能还没有在嘈杂的环境中进行评估。视觉对象跟踪具有现实世界的应用，并且在监控摄像机中的图像采集期间很有可能会添加噪声。我们的目标是研究两个国家的最先进的跟踪器在噪声的存在下，包括有效的卷积算子（ECO）和相关滤波器网络（CFNet）的鲁棒性。我们的研究表明，这些跟踪器的性能下降的噪声水平的增加，这表明需要设计更强大的跟踪算法。
英文摘要：
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades. Recent deep learning based trackers have shown good performance on various tracking challenges. A tracking method should track objects in sequential frames accurately in challenges such as deformation, low resolution, occlusion, scale and light variations. Most trackers achieve good performance on specific challenges instead of all tracking problems, hence there is a lack of general purpose tracking algorithms that can perform well in all conditions. Moreover, performance of tracking techniques has not been evaluated in noisy environments. Visual object tracking has real world applications and there is good chance that noise may get added during image acquisition in surveillance cameras. We aim to study the robustness of two state of the art trackers in the presence of noise including Efficient Convolutional Operators (ECO) and Correlation Filter Network (CFNet). Our study demonstrates that the performance of these trackers degrades as the noise level increases, which demonstrate the need to design more robust tracking algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
