Object Detection using SIFT features

Abstract

In computer vision, determining the presence and placement of objects inside an image is known as object detection. A popular feature extraction approach known as SIFT (Scale-Invariant Feature Transform) is invariant to changes in scale, rotation, and illumination. This method use a sliding window to scan the image at various scales and positions after extracting SIFT feature descriptors from the input image. To pinpoint the position of the object in the image, the SIFT descriptors are compared to the object's descriptors. It has been demonstrated that the SIFT feature detection method works well for object detection in a range of contexts, including robotics, surveillance, and face recognition. However, it also has several drawbacks, including a need for a sizable training dataset and high computing complexity. To get over these restrictions, a number of tweaks and enhancements have been suggested, including the use of quicker feature extraction methods, the use of deep learning-based techniques, and the combination of several features for greater accuracy.