Long-term tracking tasks remain challenging, especially in areas of occlusion. Herein, we propose an enhanced occlusion handling and multipeak redetection method for long-term object tracking. First, our appearance model is constructed based on two complementary cues. Each model is trained independently and combined by adaptive merging, and considers the reliability of each representation to provide a preliminary estimation. Then, we present an occlusion detection scheme relying on the response variation to activate a redetection module in case of track failure. Finally, we introduce an adaptive model update strategy using the most confident tracking predictions to retain reliable memories. The redetection module is designed based on the multipeak property of the merged response and the model is updated adaptively based on the reliability of each representation and the occlusion detection result, which allows the proposed method to deal with heavy occlusions effectively. Extensive experiments are conducted on two public benchmark datasets with 100 challenging sequences. The experimental results demonstrate that the proposed method performs favorably against 17 state-of-the-art trackers while running efficiently in real time.