Multi-level prediction Siamese network for real-time UAV visual tracking

Abstract

Existing deployed Unmanned Aerial Vehicles (UAVs) visual trackers are usually based on the correlation filter framework. Although these methods have certain advantages of low computational complexity, the tracking performance of small targets and fast motion scenarios is not satisfactory. In this paper, we present a novel multi-level prediction Siamese network (MLPS) for object tracking in UAV videos, which consists of Siamese feature extraction module and multi-level prediction module. The multi-level prediction module can make full use of the characteristics of each layer features to achieve robust evaluation of targets with different scales. Meanwhile, for small-size target tracking, we design a residual feature fusion block, which is used to constrain the low-level feature representation by using high-level abstract semantics, and obtain the improvement of the tracker's ability to distinguish scene details. In addition, we propose a layer attention fusion block which is sensitive to the informative features of each layers to achieve adaptive fusion of different levels of correlation responses by dynamically balancing the multi-layer features. Sufficient experiments on several UAV tracking benchmarks demonstrate that MLPS achieves state-of-the-art performance and runs at a speed over 97 FPS.