Object Detection in Clustered Scene Using Point Feature Matching for Non-repeating Texture Pattern

摘要

Effective object detection must be able to handle cluttered visions which convert into the object size, location, orientation, and other movements. We presumed that Computer Vision System Toolbox? MathWorks offers a variety of techniques for handling challenges in object detection. In this paper, we elaborate on how to detect an object in a cluttered scene, given a reference image of the object. The output of this paper explains an algorithm for detecting a recognized object depending on finding the vision points correspondences between reference and target images. It can detect each and every object in spite of a scale change or in-plane rotation and quite extend to robust with small amounts of out-of-plane rotation. This method of object detection through recognized feature points works best for objects that exhibit nonrepeating texture patterns, which give rise to unique feature matches. In connection with this, present algorithm is designed for detecting a specific static object only.