Review of Local Binary Pattern Histograms for Intelligent CCTV Detection

This paper presents a comprehensive review of the use of Local Binary Pattern Histogram (LBPH) in smart Closed-Circuit Television (CCTV) systems for real-time detection and analysis of abnormal events. The review covers various aspects of smart CCTV, including object detection, motion detection, and abnormal event detection. It discusses existing literature on LBPH-based methods, highlighting their strengths and weaknesses. The review emphasizes the challenges faced in smart CCTV detection, such as handling complex scenes, occlusions, lighting variations, and false alarms. It also identifies potential research directions, such as incorporating deep learning techniques, exploring multi-modal data fusion, and utilizing edge computing for real-time processing. In summary, this review offers valuable insights into the current state-of-the-art techniques for smart CCTV detection using LBPH. It provides researchers, practitioners, and policymakers involved in the development and deployment of smart CCTV systems with valuable information. The findings of this review can guide future research and contribute to the advancement of public safety and security applications.