Smart CCTV Detection Using Local Binary Pattern Histogram (LBPH)

Abstract

Smart CCTV Detection Using Local Binary Pattern Histogram (LBPH) is a computer vision technique used to improve the accuracy of object detection in video surveillance. This approach uses the LBPH algorithm with the accuracy of 97.56% to extract features from image frames captured by CCTV cameras. The LBPH algorithm is a texture-based feature extraction method that is robust to illumination changes and is capable of detecting local patterns within an image. The proposed system consists of three main stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the input image is preprocessed to enhance its quality and reduce noise. In the feature extraction stage, the LBPH algorithm is applied to the preprocessed image to extract texture features. Finally, in this study, the structural similarity index and the LBPH algorithm are proposed as Smart CCTV with intrusion detection [1]. CCTV cameras record real-time video and analyze it as it is recorded, using intrusion detection to locate illegal individuals entering our monitoring area. Experimental findings demonstrate that the suggested system achieves 97.56% high accuracy in object detection compared to existing methods. This technique has potential applications in various fields such as surveillance, security, and traffic monitoring.