Motion detection using Horn Schunck algorithm and implementation

Use of unsuitable techniques and parameters in identifying optical flow movement produces poor segmentation and indirectly affects the optical flow pattern. In this paper, emphasis is focused on the production of optical flow image using Horn Schunck technique and finding the optimum parameters. Image flow movement using Horn Schunck technique and its implementation has been researched to comprehend more about the optical flow. Simulation was performed using the simulation software called MATLAB v7.6 by Mathworks Inc. There are three types of displacements used namely small, medium and large displacement. Several important parameters such as iteration, smoothness and density have been identified to achieve the research goal. This paper reports the study on three parameters previously mentioned in combination with three different types of displacements using Horn Schunck algorithm. Based on Horn Schunck algorithm, the results were obtained after considering the segmentation results, field of optical flow, standard derivation, error and processing time. It is then identified that the optimum values of parameters are when the iteration is between 1 to 6, the smoothing is between 0.0001 to 0.002 and the density is equal to 1.