Gaussian and Laplacian of Gaussian weighting functions for robust feature based tracking

Object tracking algorithms found extensively in the computer vision literature either are inhibited by various assumptions such as simplicity of motion and shape characteristics of objects or are overly sensitive to noise. We propose and successfully test two new weighting functions for a feature-based object-tracking algorithm to achieve superior performance in tracking motion of non-rigid objects under noisy conditions. We present the implications of using the weighting functions in real and synthetic image sequences to overcome the noise produced at acquisition source (charge coupled device—CCD), or in the background environment. We also present a mechanism for determining the optimal weighting function based on image parameters, more specifically the edge characteristics of objects in the image.