An End-to-End Infrared Multi-Object Tracking Algorithm Based on Graph Attention Network

Abstract

Unmanned aerial vehicle (UAV)-based infrared target tracking has become a research hotspot with wide application prospects. However, issues such as similar target appearance features and insufficient real-time performance limit its practical deployment. To address these problems, this paper proposes an integrated framework for infrared target detection, multi-target tracking, and edge deployment. For detection, we present an anti-interference and multi-scale-aware detector. A vision Transformer (VIT)-based backbone is optimized via interference-aware and edge-aware pre-training, and a multi-scale feature encoder is designed to enhance detection robustness. For tracking, an end-to-end graph attention network-based tracker is developed. By optimizing edge feature modeling, message-passing, and graph pruning, and adopting a query decoupling strategy, the framework alleviates task coupling and enables joint optimization. Furthermore, a lightweight convolution structure is proposed to improve depthwise separable convolution, compressing the model for efficient deployment on the RK3588 platform. Extensive experiments validate the effectiveness of the proposed method, which achieves strong tracking performance and meets real-time requirements on edge devices.

Keywords

infrared target tracking / multi-scale perception / graph attention network / lightweight algorithm / hardware deployment