Lightweight object tracking algorithm based on sparse attention

Abstract

In recent years, with the widespread application of Transformer in computer vision, Transformer-based object tracking algorithms have made some progress in terms of accuracy. However, the large computation, high resource consumption, and slow inference speed of these algorithms severely limit their practical applications. Specifically, these algorithms struggle to meet the real-time tracking demands of resource-limited scenarios such as mobile devices and drones. Therefore, this paper proposes a pure Siamese-based, lightweight object tracking algorithm based on sparse attention. The proposed algorithm significantly improves tracking speed without significantly sacrificing tracking accuracy, making it suitable for practical resource-limited scenarios while still achieving good tracking performance. The proposed algorithm achieves a success rate of 76.3% and a normalized precision of 82.1% on the TrackingNet dataset, at the same time, it achieves a high inference speed of more than 100FPS, which exceeds the mainstream algorithm.