Occlusions are one of the main challenges in optical flow estimation, where parts of the scene are no longer visible between consecutive frames. Several models address this problem, either intrinsically or explicitly, using different strategies. However, most benchmarks rely on synthetic data, and even real-world ones evaluate only overall model performance, without isolating occlusions. This work investigates optical flow model performance under real-world occlusions by introducing a manually annotated, occlusion-focused dataset. We present an annotation method tailored to three occlusion types: out-of-frame, inter-object, and self-occlusion. We then evaluate two models, FlowFormer++ and CCMR, which handle occlusions using different mechanisms. Our findings show that while CCMR demonstrates stronger overall performance, both models struggle with occluded regions, particularly self-occlusions involving rotation and perspective transformations. These results highlight the need for improved occlusion reasoning in models and more diverse real-world benchmarks.