SmallTrack: Wavelet Pooling and Graph Enhanced Classification for UAV Small Object Tracking

Yuanliang Xue, Guodong Jin, Tao Shen, Lining Tan, Nian Wang, Jing Gao, Lianfeng Wang

Abstract: Aerial object tracking has recently shown great potential in the field of remote sensing. However, small objects with limited feature information pose a huge challenge to aerial trackers. Despite significant improvements, most trackers still struggle to capture enough discriminative features and overcome background disturbances. In this work, we propose an efficient aerial tracker (SmallTrack) based on the Siamese network to improve the discrimination of small objects. It consists of two effective modules, namely the wavelet pooling layer (WPL) and graph enhanced module (GEM). First, WPL decomposes the input into four subbands via wavelet domain learning and fully utilizes the high- and low-frequency information in the subbands to preserve the discriminative features of small objects. Second, GEM embeds the pixels on the classification responses as nodes in graph learning through graph neural networks (GNNs), which naturally mines the similarity between pixels. Based on the pixel-level modulation constructed from graph theory, GEM enhances the understanding of small objects and highlights them in the classification responses. The proposed tracker achieves leading performance on five aerial benchmarks, while maintaining a high running speed of 72.5 frames/s. Besides, real-world tests on an aerial platform have proven the effectiveness of SmallTrack. The code and models are available at https://github.com/xyl-507/SmallTrack.