<p>The accurate estimation of motion in video sequences is a critical task in various computer vision applications, such as video compression, video surveillance, and autonomous navigation. Block matching is a popular motion estimation method through which we find the best match between blocks of pixels in consecutive frames. However, using a single block matching algorithm for the entire frame may be inefficient, especially when dealing with dynamic scenes containing moving objects. In this paper, we propose a novel approach for motion estimation using composite block-matching algorithms based on object detection. Our method first detects the moving objects in the video frame using a deep learning-based object detection algorithm. Then, we use one block-matching algorithm for the area of the frame where the objects are detected and another block-matching algorithm for the remaining part of the frame. The rationale behind using two different algorithms is that the area containing the objects has more complex motion patterns and may require a more sophisticated and rigorous algorithm than the rest of the frame. Our results show that the proposed method achieves higher accuracy and faster processing time compared to any single motion estimation method. The proposed composite block matching algorithm provides more accurate motion estimation in the object regions, leading to better object tracking and fewer motion artefacts. Moreover, using a simpler block-matching algorithm for the non-object regions reduces computational overhead, making the overall process more efficient for compressing surveillance video.</p>