An Investigation on Derivative and Model Noise for Optical Flow
Abstract
This thesis presents the implementation and the quantitative and qualitative evaluation of three optical flow methods frequently used. We propose using a weight matrix of noise with three algorithms in computing optical flow. Our methods are implemented in traditional and hierarchical approaches of the Horn-Schunck and the Lucas-Kanade, and the Brox, respectively. Five different derivatives are used to compose the weight matrix, then estimating optical flow. We present quantitative results and give a qualitative evaluation based on various datasets. The tested data can be categorized into two groups, real imagery of nonrigidly moving scenes and realistic synthetic imagery. Our evaluation concentrates on several benchmarks such as angular error. The results showed that our proposed methods can produce a more precise flow field than that of original approaches.