Aiming at the problem of frequent identity switching and trajectory interruption caused by occluded targets in infrared scenarios, an improved YOLOv12 and StrongSORT-based algorithm named AntiOcc-YOLOv12-StrongSORT was proposed for infrared occluded object tracking and counting. During the detection stage, a repulsion loss function was integrated into YOLOv12 to enhance localization and distinguish adjacent bounding boxes, improving detection accuracy under occlusion. During the tracking and counting stage, an occlusion-aware mechanism was proposed within the StrongSORT framework. The occlusion state of targets was determined by combining the geometric features of detection boxes and the target’s infrared thermal radiation characteristics, thereby enhancing the perception of occluded targets and effectively mitigating identity loss caused by occlusion. Anti-occlusion dynamic template update strategy was proposed. During the update of the target appearance template, the weights of historical templates were adaptively adjusted based on the target’s occlusion state, significantly suppressing the cumulative error caused by occlusion in the target appearance template. A virtual region counting strategy was introduced based on tracking results to achieve accurate counting of infrared occluded targets. Experimental results indicate that compared to various tracking algorithms integrating YOLOv12 as the detection algorithm, such as Bytetrack, BoostSORT, DeepOCSORT, and StrongSORT, the AntiOcc-YOLOv12-StrongSORT algorithm achieves optimal values in multiple tracking metrics, including multiple object tracking accuracy (MOTA), multiple object tracking precision (MOTP), identification F1 score(IDF1), and identification switches(IDs). Furthermore, it demonstrates sustained and stable counting performance in complex occluded infrared scenarios.