Visual Object Tracking (VOT) remains challenging under occlusion scenarios, where traditional trackers often suffer from feature degradation and target loss. To address this issue, we propose OASAMT, an occlusion-aware tracking framework that equips SAM2 with explicit temporal occlusion reasoning via two Temporal Convolutional Networks (TCNs) and a Dual-Memory Bank (DMB). Specifically, two TCN-based modules are designed to model temporal occlusion dynamics: the Temporal Occlusion Classifier (TOC) for inferring target occlusion states using confidence scores, mask IoU, and area ratio; and the Temporal Occlusion Predictor (TOP) for forecasting target bounding boxes during occlusion. The proposed DMB consists of a Non-Occlusion Memory Bank (N-OMB) and an Occlusion Memory Bank (OMB), explicitly decoupling reliable and occluded representations to prevent memory contamination and improve re-detection after occlusion. Additionally, to facilitate systematic evaluation under occlusion scenarios, we construct OccTrack, a dedicated occlusion-oriented dataset derived from four UAV-view benchmarks. Extensive experiments were conducted on the OccTrack, LaSOT, LaSOText, and GOT-10k datasets. The results demonstrate that OASAMT consistently outperforms SAM2.1 and other advanced trackers in both occlusion-specific and general tracking scenarios. The code and the dataset are available at https://github.com/ChaseFalcon99/OASAMT.