Fusion Tree Network for RGBT Tracking

Zhiyuan Cheng, Andong Lu, Zhang Zhang, Chenglong Li, Liang Wang
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引用次数: 1

Abstract

RGBT tracking is often affected by complex scenes (i.e., occlusions, scale changes, noisy background, etc). Existing works usually adopt a single-strategy RGBT tracking fusion scheme to handle modality fusion in all scenarios. However, due to the limitation of fusion model capacity, it is difficult to fully integrate the discriminative features between different modalities. To tackle this problem, we propose a Fusion Tree Network (FTNet), which provides a multi-strategy fusion model with high capacity to efficiently fuse different modalities. Specifically, we combine three kinds of attention modules (i.e., channel attention, spatial attention, and location attention) in a tree structure to achieve multi-path hybrid attention in the deeper convolutional stages of the object tracking network. Extensive experiments are performed on three RGBT tracking datasets, and the results show that our method achieves superior performance among state-of-the-art RGBT tracking models.
基于融合树网络的rbt跟踪
rbt跟踪经常受到复杂场景的影响(如遮挡、尺度变化、背景噪声等)。现有的工作通常采用单一策略的RGBT跟踪融合方案来处理所有场景下的模态融合。然而,由于融合模型能力的限制,难以充分融合不同模式之间的判别特征。为了解决这一问题,我们提出了一种融合树网络(FTNet),它提供了一种具有高容量的多策略融合模型,可以有效地融合不同的模式。具体而言,我们将三种注意模块(通道注意、空间注意和位置注意)组合成树形结构,在目标跟踪网络的深层卷积阶段实现多路径混合注意。在三个RGBT跟踪数据集上进行了大量的实验,结果表明我们的方法在最先进的RGBT跟踪模型中具有优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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