Motion Estimation is the most computationally demanding aspect of the video encoding process. As video encoding is essential for storage and transmission for videos. Various algorithms as Fast Block Matching Algorithms have been developed to reduce the computation and improve the performance. The challenge is to decrease the computational load on the system without compromising the quality of the video stream especially for real time applications. The paper proposed an evaluation of the adaptation of motion estimation algorithms to the type of motion detected in real time video sequences. The video sequence may have different types of motion, static, slow or fast motion, the algorithms used must be adapted with the scene change, and a scene change detection method has been encountered. So the adapting of motion estimation algorithms to the type of motion detected in the video scenes is required to be applied till the end of real time video streaming. This adaptation process is done continuously, keeping into consideration the tradeoff between achieving acceptable computational time by minimizing the computational complexity and achieving a good accuracy for image quality. Eight methods have been discussed and implemented ranging from the very basic exhaustive search to the fast adaptive algorithms, and they are evaluated and compared together in terms of PSNR (peak signal to noise ratio) and computational complexity