In recent years, there has been significant progress in Visual Object Tracking with evolutions of both computers and learning algorithms, especially in Neural Networks. Therefore, we obtain better results by combining Neural Networks and traditional tracking methods such as Kalman Filter and Correlation Filters. SiamFC is an example of such algorithms because SiamFC combines Siamese Neural Networks and Correlation Filters. SiamFC is open to development because it does not have an online learning process. An example of the improved SiamFC is Kalman-Siam that combines Kalman Filter and Multi-feature SiamFC. Kalman-Siam uses Kalman-Filter to solve the occlusion situation problem by processing the target's previous motion trajectory. Therefore, the tracking can fail in other complex scenarios for Kalman-Siam. One of the methods for solving such problems is detecting this situation and starting the re-tracking process as we used in this research. Also, we used a parameter calculated on the response map after the correlation operation in SiamFC to detect these situations. First, our algorithm generates possible prioritized search windows. Then, it runs in a specific order of priority for these generated search windows surrounding the target's last known location. We named this process Adaptive Window Search that starts from the highest priority search windows and continues until the lowest search windows do not exist. Therefore, we named our algorithm Adaptive-Kalman-Siam. We demonstrated more successful results on commonly used datasets. Adaptive-Kalman-Siam tracks an object better than SiamFC and Kalman-Siam in Background Clutters, Fast Motion, Motion Blur, and Occlusion complex tracking scenarios.