INTRODUCTIONMoving object detection finds many applications in surveillance and traffic monitoring. There are many object detection methods. Object detection typically involves keyframe extraction and background subtraction. Keyframe extraction summarizes the video by eliminating transition frames, thereby reducing the computational load. Methods based on image difference, local binary differential, wavelet and histogram are some of the methodologies used to obtain keyframes. KEY FRAME EXTRACTION Frames that contain all the important information of a video clip are called key frames of that video clip. Today, video footage with a high frame rate and high resolution. A video with a frame rate of 25 fps and a grayscale image of 1920 x 1080 produces approximately 6 MB of data every second. The amount of data to be processed can be reduced by choosing only key frames from the shot video. Keyframes are a set of frames that summarize a video shot. Applying image processing algorithms only to keyframes reduces the computational load with little loss of generality. A video sequence containing a moving object has many transition frames that have very minimal displacement from its position in the previous frame. Keyframe extraction techniques remove frames that do not show significant changes from previous frames while retaining essential information required for humans. eye to judge the direction and apparent change in movement of the object. Such techniques find applications in video compression, content-based video retrieval, and video classification. Most keyframe extraction algorithms involve finding the distance between frames and comparing it with the threshold. Wavelet transform, local binary pattern (texture b...... middle of paper ......e Hui, "An improved clever edge detection algorithm based on a predisposition method for the image corrupted by the Gaussian noise", World Automation Congress (WAC), 2010, vol., no., pp.113, 116, September 19-23, 2010. Caixia Deng Weifeng Ma, "An edge detection approach to fusion 'images based on an improved Sobel operator', Image and Signal Processing (CISP), 4th International Congress 2011, vol. 3, no., pp. 15-17. Zhang, “Adaptive Fast Gaussian Background Subtraction Algorithm”, Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on, vol., no., pp.766,771, December 29-31, 2012Xiaochun Zou, “A Robust Background Subtraction Approach with a Moving Camera,” Computing and Convergence Technologies (ICCCT), 2012 7th International Conference on, vol., no., pp.1026,1029, December 3-5. 2012