针对煤矿井下行人因遮挡频繁和外观混淆导致轨迹匹配不准确的问题，提出了一种基于自适应链接优化的井下行人抗遮挡跟踪方法。首先，根据目标置信度变化率和交并比计算，对目标进行遮挡判定，筛选出潜在遮挡目标。然后，在匹配级联阶段，引入潜在遮挡目标的非线性动态特征，并结合历史轨迹信息扩展轨迹链接优化模块的轨迹对输入，同时在轨迹对输入进行时域块处理后添加通道先验卷积注意力机制，增强时域表征能力。轨迹对输入向量经压缩与融合处理后，由多层感知器输出轨迹相似性得分，与原有匹配级联阶段中卡尔曼滤波器的总成本函数相结合，优化匹配决策，有效缓解轨迹匹配过程中的错误匹配问题。最后，在交并比匹配阶段，通过计算断裂率和ID切换率的变化量，引入自适应RB因子，构建反馈机制，动态调整匹配决策中的交并比阈值，以适应因长时间遮挡导致的轨迹断裂问题。采用所提方法与DeepSORT，YOLOv7−SAM，OSNet，FuCoLoT对煤矿井下典型视频序列进行对比实验，结果表明，所提方法的跟踪准确度(MOTA)，跟踪精度(MOTP)和身份F1(IDF1)分别为76.17%，84.13%，74.9%，较DeepSORT分别提升了14.9%，1.83%和10.93%，较YOLOv7−SAM分别提升了1.57%，0.4%和0.37%，较OSNet分别提升了2.83%，0.77%和1.27%，较FuCoLoT分别提升了2.5%，0.08%和1.8%，说明所提方法能够有效解决煤矿井下目标在遮挡情形下的跟踪误匹配问题。

To address the issue of inaccurate trajectory matching caused by frequent occlusions and appearance confusion of underground mine personnel in coal mines, an anti-occlusion tracking method for underground mine personnel based on adaptive link optimization was proposed. Firstly, occlusion detection of the targets was performed based on the target confidence change rate and intersection-over-union (IoU) calculation to identify potential occluded targets. Secondly, in the matching cascade stage, nonlinear dynamic features of potential occluded targets were introduced, and historical trajectory information was incorporated to expand the trajectory pair input for the trajectory link optimization module. Additionally, after performing time-domain block processing on the trajectory pair input, a channel prior convolutional attention mechanism was added to enhance the time-domain representation capability. After compression and fusion processing of the trajectory pair input vectors, a trajectory similarity score was output by the multilayer perceptron. This score was combined with the total cost function of the Kalman filter in the original matching cascade stage to optimize matching decisions, effectively alleviating the issue of incorrect matching during the trajectory matching process. Finally, in the IoU matching stage, an adaptive RB factor was introduced by calculating the variations in fracture rate and ID switch rate to construct a feedback mechanism. This mechanism dynamically adjusted the IoU threshold in the matching decision to address trajectory fragmentation caused by long-term occlusion. Comparative experiments were conducted on typical video sequences from underground coal mines using the proposed method, DeepSORT, YOLOv7-SAM, OSNet, and FuCoLoT. The results showed that the proposed method achieved the multiple object tracking accuracy (MOTA) of 76.17%, the multiple object tracking precision (MOTP) of 84.13%, and the identity F1 (IDF1) of 74.9%. Compared to DeepSORT, these values improved by 14.9%, 1.83%, and 10.93%, respectively. Compared to YOLOv7-SAM, they improved by 1.57%, 0.4%, and 0.37%, respectively. Compared to OSNet, they improved by 2.83%, 0.77%, and 1.27%, respectively. Compared to FuCoLoT, they improved by 2.5%, 0.08%, and 1.8%, respectively. This demonstrates that the proposed method can effectively address the issue of tracking mismatches in occlusion scenarios in underground coal mine targets.