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Recently, Siamese-based trackers have achieved excellent performance in object tracking. However, the high speed and deformation of objects in the movement process make tracking difficult. Therefore, we have incorporated cascaded region-proposal-network (RPN) fusion and coordinate attention into Siamese trackers. The proposed network framework consists of three parts: a feature-extraction sub-network, coordinate attention block, and cascaded RPN block. We exploit the coordinate attention block, which can embed location information into channel attention, to establish long-term spatial location dependence while maintaining channel associations. Thus, the features of different layers are enhanced by the coordinate attention block. We then send these features separately into the cascaded RPN for classification and regression. According to the two classification and regression results, the final position of the target is obtained. To verify the effectiveness of the proposed method, we conducted comprehensive experiments on the OTB100, VOT2016, UAV123, and GOT-10k datasets. Compared with other state-of-the-art trackers, the proposed tracker achieved good performance and can run at real-time speed.

Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention