Study on intelligent anti-occlusion tracking algorithm for infrared ground targets

红外地面目标智能抗遮挡跟踪算法研究

¹ Luoyang Institute of Electro-Optical Equipment, AVIC, Luoyang 471000, China
² Unmaned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China

Received: 10 July 2023

Abstract

In response to the issue of infrared ground target tracking failure caused by background occlusion, a novel anti-occlusion tracker for infrared ground targets is proposed based on an enhanced trajectory prediction network. Initially, an occlusion assessment criterion is proposed to accurately assess the occlusion status of infrared ground targets. Subsequently, enhancements are made to the BiTrap trajectory prediction network. On one hand, velocity information is introduced through a Siamese network structure, adopting a unidirectional prediction method, building the SiamTrap trajectory prediction network that improves trajectory prediction accuracy. On the other hand, refining both the training and application methods enables more precise predictions of ground target trajectories. For short-term occlusion, the SiamTrap network uses temporal context information to predict the occluded position of the target. For long-term occlusion, a search expansion strategy is introduced to address prediction errors accumulated due to a lack of real target information. Finally, a "second verification" criterion is introduced, realizing accurate target capture and normal tracking. Comparative tests are conducted on infrared target tracking sequences with occlusion. Compared to baseline trackers, the proposed algorithm shows a 5.2% improvement in success rate and a 5.9% improvement in accuracy under the OPE evaluation metric. This indicates the robustness of the proposed algorithm in handling occlusion scenarios for infrared ground targets.

摘要

针对背景遮挡导致红外地面目标跟踪失败的问题, 提出了一种基于改进轨迹预测网络的红外地面目标抗遮挡跟踪器。提出了遮挡判断准则, 准确判断红外地面目标的遮挡情况; 改进BiTrap轨迹预测网络, 一方面通过孪生网络结构引入速度信息, 采用单向预测的方法, 提出了SiamTrap轨迹预测网络, 提高了轨迹预测的精度; 另一方面, 通过改进训练方法和应用方法, 可以更准确地预测地面目标的轨迹。对于短期遮挡, 利用SiamTrap网络基于时间上下文信息预测目标遮挡位置。对于长期遮挡, 提出了搜索扩展策略来处理真实目标信息缺乏导致的预测误差积累。提出了“二次判定”准则, 实现了目标的精确捕获和正常跟踪。在含有遮挡的红外目标跟踪序列上对算法进行了对比测试。与基准跟踪器相比, 文中所提算法在OPE评价指标下, 成功率和准确率分别提高了5.2%和5.9%。这表明文中算法在应对红外地面目标被遮挡情况下具有良好的鲁棒性。