Visual tracking of multiple pedestrians in video sequences is an important procedure for many computer vision applications. The tracking-by-detection approach is widely used for visual pedestrian tracking. This approach extracts pedestrian regions from each video frame and associates the extracted regions across frames as the same pedestrian according to the similarities of region features (e.g., position, appearance, and movement). When a pedestrian is temporarily occluded by a still obstacle in the scene, he/she disappears at one side of the obstacle in a certain frame and then reappears at the other side of it a few frames later. The occlusion state of the pedestrian, that is the space-time interval where the pedestrian is missing, varies with obstacle areas and pedestrian movements. Such an unknown occlusion state complicates the region association process for the same pedestrian and makes the pedestrian tracking difficult. To solve this difficulty and improve pedestrian tracking robustness, we propose a novel method for tracking pedestrians while estimating their occlusion states. Our method acquires obstacle areas by the pedestrian regions extracted from each frame, estimates the occlusion states from the acquired obstacle areas and pedestrian movements, and reflects the estimated occlusion states in the region association process.