Video surveillance for the crime detection using features

Abstract

This paper aims at extending the comparison between two images and locating the query image in the source image by matching the features in the videos by presenting a method for the recognition of a particular person or an object. The frames matching the feature (not feature its query) object in a given video will be the output. We describe a method to find unique feature points in an image or a frame using SIFT, i.e., scale-invariant feature transform method. SIFT is used for extracting distinctive feature points which are invariant to image scaling or rotation, presence of noise, changes in image lighting, etc. After the feature points are recognized in an image, the image is tracked for comparison with the feature points found in the frames. The feature points are compared using homography estimation search to find the required query image in the frame. In case the object is not present in the frame, then it will not present any output.