SIFT Feature-Based Video Camera Boundary Detection Algorithm

Lingqiang Kong,Wei Wang

doi:10.1155/2021/5587873

Abstract

Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study.

Highlights

As video is the most complex of all multimedia data types, it contains the content of the still image and includes the motion information of the target in the scene and the information of the objective world changing with time. e huge amount of data and the unstructured characteristics of film and television make it extremely difficult to carry out effective film and television retrieval. e traditional film and television retrieval mainly relies on the manual definition of the key words of the film and television
A new method of lens edge detection based on dynamic texture and SIFT features is proposed for many kinds of film and television data. e algorithm mainly includes four aspects: movie and television dynamic texture construction, movie and television dynamic texture matching, frame image SIFT feature matching, and false detection processing
E dynamic texture of film and television takes into account the local and global changes of the frame image. e method presented in this paper is effective for edge detection of both shear and gradient lenses, especially for edge detection of laminated lenses, and for reducing the influence of light on lens detection

Summary

Introduction

As video is the most complex of all multimedia data types, it contains the content of the still image and includes the motion information of the target in the scene and the information of the objective world changing with time. e huge amount of data and the unstructured characteristics of film and television make it extremely difficult to carry out effective film and television retrieval. e traditional film and television retrieval mainly relies on the manual definition of the key words of the film and television. Video allows the user to use Complexity visual characteristics and to retrieve the relationship between time and space and film and television and has integrated text and visual search method, automatic video object segmentation and tracking, and rich visual feature library, including color, texture, shape, and motion, interactive query, and browsing on the Internet [7, 8]. E histogram-based lens detection algorithm, from the statistical point of view, statistics the pixel color distribution of the frame image or the gray distribution of the image, can better adapt to the lowspeed motion of the camera equipment and the object in the lens It can effectively reduce the impact of flash, subtitle insertion, advertising, and other factors on the lens detection and reduce the impact of the lens movement on the boundary detection of the film and television, further enhancing the robustness of the lens detection. e detection effect of the algorithm is verified by experiments

Dynamic Texture Boundary Detection Based on SIFT Feature

Objective characteristics Picture diversity

Example Verification

C2 C3 C4 C5 C6

Findings

Conclusion