Single-Object Tracking (SOT) is a fundamental problem in computer vision, where the goal is to track the movement of a single object across a sequence of frames in a video. This problem is challenging due to various factors such as object appearance changes, occlusions, and varying lighting conditions. The task becomes even more difficult when tracking objects in real-time or on resource-constrained devices, where computational efficiency is a critical concern. In recent years, Siamese networks have emerged as a powerful framework for SOT due to their ability to learn discriminative features from pairs of images. However, while Siamese networks have shown great promise, challenges remain in improving their efficiency and tracking performance, especially in terms of handling large amounts of data and maintaining robustness in dynamic environments. The proposed approach, a lightweight Siamese network with global correlation for single-object tracking, seeks to address these challenges by focusing on computational efficiency and performance. The key to the success of this approach lies in combining the strengths of Siamese networks with a global correlation mechanism, which enhances the ability to capture long-range dependencies between objects in different frames. By leveraging a lightweight network architecture and global correlation, the model can achieve high tracking accuracy while minimizing computational cost, making it suitable for real-time applications and deployment on devices with limited resources.
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