论文标题
自适应RGBT跟踪的时间聚集
Temporal Aggregation for Adaptive RGBT Tracking
论文作者
论文摘要
使用RGB和热红外（TIR）光谱在RGBT跟踪中可用的视觉对象跟踪是一个新颖而充满挑战的研究主题，如今引起了人们越来越多的关注。在本文中，我们提出了一个RGBT跟踪器，该跟踪器将时空线索构成了稳健的外观模型学习，同时构建了用于交叉模式相互作用的自适应融合子网络。与仅包含空间信息的大多数现有的RGBT跟踪器不同，在此方法中进一步考虑了时间信息。 Specifically, different from traditional Siamese trackers, which only obtain one search image during the process of picking up template-search image pairs, an extra search sample adjacent to the original one is selected to predict the temporal transformation, resulting in improved robustness of tracking performance.As for multi-modal tracking, constrained to the limited RGBT datasets, the adaptive fusion sub-network is appended to our method at the decision level to reflect the complementary characteristics以两种方式包含。为了设计热红外辅助RGB跟踪器，在RGB模式的残留连接之前，请考虑从TIR模式中分类头的输出。对三个具有挑战性的数据集的广泛实验结果，即GTOT-RGBT2019，GTOT和RGBT210，验证了我们方法的有效性。代码将以\ textColor {blue} {\ emph {https://github.com/zhangyong-tang/taat}}共享。
Visual object tracking with RGB and thermal infrared (TIR) spectra available, shorted in RGBT tracking, is a novel and challenging research topic which draws increasing attention nowadays. In this paper, we propose an RGBT tracker which takes spatio-temporal clues into account for robust appearance model learning, and simultaneously, constructs an adaptive fusion sub-network for cross-modal interactions. Unlike most existing RGBT trackers that implement object tracking tasks with only spatial information included, temporal information is further considered in this method. Specifically, different from traditional Siamese trackers, which only obtain one search image during the process of picking up template-search image pairs, an extra search sample adjacent to the original one is selected to predict the temporal transformation, resulting in improved robustness of tracking performance.As for multi-modal tracking, constrained to the limited RGBT datasets, the adaptive fusion sub-network is appended to our method at the decision level to reflect the complementary characteristics contained in two modalities. To design a thermal infrared assisted RGB tracker, the outputs of the classification head from the TIR modality are taken into consideration before the residual connection from the RGB modality. Extensive experimental results on three challenging datasets, i.e. VOT-RGBT2019, GTOT and RGBT210, verify the effectiveness of our method. Code will be shared at \textcolor{blue}{\emph{https://github.com/Zhangyong-Tang/TAAT}}.
