Object Tracking with Anchor-Free and Online Updating

doi:10.3778/j.issn.1002-8331.2102-0098

Abstract

Abstract:

SiamRPN is an anchor-based tracking algorithm, which is not robust to scale change, severe deformation, and rotation. This paper proposes a target tracking algorithm with anchor-free and online updating. Firstly, a multi-layer fusion feature extraction network is proposed, which can make full use of the structure and semantic information of the image. Secondly, an anchor-free mechanism is adopted, which enables the network to directly predict the distance from the sampling points in the target area to the boundary of the target area. The anchor-free mechanism can avoid the shortcomings of the anchor-based mechanism. Finally, an online updating module is added to the backbone network. The online updating module uses the latest tracking results for online training, so that the algorithm can better predict targets that do not appear in the training set, furthermore it can make the algorithm adapt to the change of the targets during tracking. Compared with SiamRPN algorithm, the success rate and accuracy rate of this algorithm on OTB100 data set are increased by 0.062 and 0.065. It shows better robustness to scale change, severe deformation and rotation.

Key words: multi-layer fusion, anchor-free mechanism, online updating, Siamese network

摘要：

SiamRPN这种基于锚点机制的跟踪算法对目标尺度变化、剧烈形变以及旋转等问题鲁棒性不强，针对此问题提出了一种基于无锚点机制与在线更新的目标跟踪算法。提出了一种多层融合的特征提取网络，该网络能充分利用图像的结构与语义信息；采用了一种无锚点机制，使网络能够直接预测出目标区域内采样点到目标区域边界的值，有效避免了锚点机制的相关缺点；在主干网络的基础上添加了在线更新模块，利用最新的跟踪结果进行在线训练，使算法能更好地预测未在训练集中出现的目标，并进一步适应目标的变化。相较于SiamRPN算法，改进算法在OTB100数据集上，成功率与准确率分别提高了0.062与0.065，对目标的尺度变化，剧烈形变以及旋转等问题表现出了更好的鲁棒性。

关键词: 多层特征融合, 无锚点机制, 在线更新, 孪生神经网络

ZHANG Rui, SONG Jingzhou, LI Sihao. Object Tracking with Anchor-Free and Online Updating[J]. Computer Engineering and Applications, 2021, 57(20): 210-220.

张睿，宋荆洲，李思昊. 基于无锚点机制与在线更新的目标跟踪算法[J]. 计算机工程与应用, 2021, 57(20): 210-220.

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