Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
Suzhou Academy, Xi'an Jiaotong University, Suzhou, China.
PLoS One. 2020 Apr 13;15(4):e0231087. doi: 10.1371/journal.pone.0231087. eCollection 2020.
Accurate visual tracking is a challenging issue in computer vision. Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance. Nonetheless, CF-based trackers are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking challenge. In this paper, we presented a very powerful tracker based on the kernelized correlation filter tracker (KCF). Firstly, we employ an intelligent multi-part tracking algorithm to improve the overall capability of correlation filter based tracker, especially in partial-occlusion challenges. Secondly, to cope with the problem of scale variation, we employ an effective scale adaptive scheme, which divided the target into four patches and computed the scale factor by finding the maximum response position of each patch via kernelized correlation filter. With this method, the scale computation was transformed into locating the centers of the patches. Thirdly, because the small deviation of the central function value will bring the problem of location ambiguity. To solve this problem, the new Gaussian kernel functions are introduced in this paper. Experiments on the default 51 video sequences in Visual Tracker Benchmark demonstrate that our proposed tracker provides significant improvement compared with the state-of-art trackers.
精确的视觉跟踪是计算机视觉中的一个具有挑战性的问题。基于相关滤波器 (CF) 的方法在视觉跟踪中因其效率和高性能而受到关注。然而，基于 CF 的跟踪器对部分遮挡很敏感，这可能会降低它们的整体性能，甚至导致跟踪挑战失败。在本文中，我们提出了一种基于核相关滤波器跟踪器 (KCF) 的非常强大的跟踪器。首先，我们采用智能多部分跟踪算法来提高基于相关滤波器的跟踪器的整体性能，特别是在部分遮挡的挑战中。其次，为了解决尺度变化的问题，我们采用了一种有效的尺度自适应方案，将目标分为四个部分，并通过核相关滤波器找到每个部分的最大响应位置来计算尺度因子。通过这种方法，尺度计算转化为定位补丁的中心。第三，由于中心函数值的小偏差会带来位置模糊的问题。为了解决这个问题，本文引入了新的高斯核函数。在 Visual Tracker Benchmark 的默认 51 个视频序列上的实验表明，与最先进的跟踪器相比，我们提出的跟踪器有了显著的改进。
