Detection of Objects in Cluttered Scenes Using Matching Technique

Abstract

Detection of objects in cluttered scenes is a fundamental challenge that has only recently been widely undertaken by computer vision systems. This paper proposes a novel method how to detect a particular object in cluttered scenes, given a reference image of the object. This paper presents an algorithm for detecting a specific object based on finding point correspondences between the reference and the target image. It can detect objects despite a scale change or in-plane rotation. It is also robust to small amount of out-of-plane rotation and occlusion. Vision systems are increasingly used in the fields of industrial automation and home robotics. Real-time object learning and detection are important and challenging tasks in Computer Vision. Among the application fields that drive development in this area, robotics especially has a strong need for computationally efficient approaches, as autonomous systems continuously have to adapt to a changing and unknown environment, and to learn and recognize new objects. For such time-critical applications, point feature matching is an attractive solution because new objects can be easily learned online, in contrast to statistical-learning techniques that require many training samples. Our approach is related to recent and efficient matching methods and more particularly to, which consider only images and their gradients to detect objects. The method of object detection works best for objects that exhibit non-repeating texture patterns, which give rise to unique feature matches. This technique is not likely to work well for uniformly- colored objects, or for objects containing repeating patterns. The proposed algorithm is designed for detecting a specific object, for example, the elephant in the reference image, rather than any elephant. For detecting objects of a particular category, such as people or faces etc, The structure of the paper is as follows: Section II gives an algorithm implementation. Section III gives simulation results. Section IV gives conclusion.