Gong-kuang zidonghua (Feb 2025)
Research on anti-occlusion tracking method for underground mine personnel based on adaptive link optimization
Abstract
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.
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