Visual SLAM systems often suffer from degraded performance in dynamic environments due to the inclusion of feature points on moving objects, which violate the static world assumption. In this paper, we propose a dynamic object filtering framework designed to enhance the performance of monocular ORB-SLAM3 by excluding features associated with dynamic regions. Our method integrates deep learning-based object detection, multi-object tracking, and dense optical flow analysis to generate per-frame binary masks that identify and suppress dynamic content. A motion similarity metric, which combines directional- and magnitude-based flow differences with spatial weighting, is used to assess object motion relative to the background. Temporal consistency is enforced through a probabilistic state filter to smooth the classification over time. We evaluated our approach on the KITTI Odometry dataset, and it shows that our solution significantly reduced pose estimation errors in highly dynamic sequences, while maintaining performance in static scenes. The results demonstrate the effectiveness of the proposed filtering strategy as a lightweight and general-purpose enhancement to existing SLAM systems when operating in real-world environments.