结合注意力与特征融合网络调制的视觉跟踪算法

Visual Tracking Combining Attention and Feature Fusion Network Modulation

针对现有网络调制类的跟踪算法忽略高阶特征信息从而在应对大尺度变化、物体形变时易发生漂移的现象,提出了一种结合注意力与特征融合网络调制的目标跟踪算法。首先,将一个高效的选择核注意力模块嵌入在特征提取的主干网络中,使网络更关注于对目标特征信息的提取;其次,对提取的特征采用多尺度交互网络充分挖掘层内多尺度信息,并且融合高阶特征信息来提升对目标的表征能力,以适应跟踪过程中复杂多变的环境;最后,通过金字塔调制网络引导测试分支学习最优交并比预测,实现对目标的精确估计。实验结果表明,在VOT2018、OTB100、GOT10k、TrackingNet和LaSOT视觉跟踪基准上,相比其他算法,所提算法在跟踪精度和成功率上展现了较强的竞争力。

The existing tracking algorithms for network modulation ignore high order feature information, so they are prone to drift when dealing with large scale changes and object deformations. An object tracking algorithm that combines the attention mechanism and feature fusion network modulation is proposed. First, an efficient selective kernel attention module is embedded in the feature extraction backbone network, so that the network pays more attention to the extraction of target feature information;second, a multiscale interactive network is used for the extracted features to fully mine the multiscale information in the layer, and high order feature information is fused to improve the ability of target representation, to adapt to the complex and changeable environment in the tracking process;finally, the pyramid modulation network is used to guide the test branch to learn the optimal intersection over union prediction to achieve an accurate estimation of the targets. Experimental results show that the proposed algorithm achieves more competitive results than other algorithms in tracking accuracy and success rate on VOT2018, OTB100, GOT10k, TrackingNet, and LaSOT visual tracking benchmarks.