In recent years, discriminative correlation filters (DCF) and Siamese networks based trackers have significantly advanced the performance in tracking. However, the problem of accurate target state estimation is not fully solved yet. Therefore, in this paper, we propose a Criss-Cross Location Attention (CCLA) module, which pays more concerns to global and local contextual information and is used for the adaptation of IoU-Net based trackers. Besides, our CCLA module has capability of high computational efficiency with a slight increase of network parameters. Then, we present our tracker called ATCC, a Siamese architecture with CCLA. Finally, we evaluate our tracker on OTB100, VOT-2018, LaSOT, and TrackingNet benchmark datasets. Experimental results show that our tracker performs favorably against other state-of-the-art trackers, while operating at 30 FPS on single GPU. We will release the code and models at https://github.com/yongwuSHU/atcc.

近年来，基于判别相关滤波器（DCF）和基于暹罗网络的跟踪器已经大大提高了跟踪性能。但是，准确的目标状态估计问题尚未完全解决。因此，在本文中，我们提出了一个Criss-Cross Location Attention（CCLA）模块，该模块更加关注全局和局部上下文信息，并用于基于IoU-Net的跟踪器的改编。此外，我们的CCLA模块具有较高的计算效率，并且网络参数略有增加。然后，我们介绍称为ATCC的跟踪器，它是CCLA的暹罗体系结构。最后，我们在OTB100，VOT-2018，LaSOT和TrackingNet基准数据集上评估跟踪器。实验结果表明，我们的跟踪器的性能优于其他最新的跟踪器，在单个GPU上以30 FPS的速度运行时。我们将在https://github.com/yongwuSHU/atcc上发布代码和模型。