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

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.