Detecting the Object from Clutter using Speeded up Robust Feature

Abstract

In our hectic day to day life, there are many things that we forget or want to identify from a clutter. If we spend lot of time searching for misplaced objects, then we may not be able to do our work at the right time. Suppose if we have a technique to find whether the object that we are looking for is there in a specified area, lying with a clutter of things, then we save a lot of time. This paper highlights a concept that can detect the lost product by knowing the features. The paper presents the detection of objects based on the SURF (Speeded up Robust Feature) features. It is a novel scale- and rotation-invariant interest point detector and descriptor. It became more important because of its repeatability, distinctiveness, and robustness, and the computation time is much faster. This paper presents the experimental result of the object detection and its accuracy. Here, the images are taken in the context of real-life situations.