In conventional object tracking methods, much attention has been paid to tracking efficiency, but they often failed in tracking an occluded object. In this paper, we present a new method to improve the occlusion adaptability and tracking robustness. This proposed algorithm covers the occlusion-adaptive particle filter (OAPF) framework, which employs the adaptive state transition model to detect occlusions by a first-order histogram difference dynamic algorithm accurately and simply. Thus, when partial or complete occlusions occur, it can detect interrupted state transition to realize persistent tracking. In addition, tracking robustness is also upgraded via adaptive Gaussian noise coefficient model in particle propagation. Finally, we emphasize that the computing complexity of OAPF is evidently decreased by reducing the particle number in execution. As a result, this simple and effective occlusion-adaptive tracking method has been demonstrated through several real-time sequences.