TY - GEN
T1 - Review of Local Binary Pattern Histograms for Intelligent CCTV Detection
AU - Sharma, Deepak
AU - Singh, Brajesh Kumar
AU - Suryani, Erma
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - LBPH
KW - Object detection
KW - Recognition
KW - Smart CCTV
UR - http://www.scopus.com/inward/record.url?scp=85181981280&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-6906-7_24
DO - 10.1007/978-981-99-6906-7_24
M3 - Conference contribution
AN - SCOPUS:85181981280
SN - 9789819969050
T3 - Lecture Notes in Networks and Systems
SP - 277
EP - 286
BT - Advances in Data and Information Sciences - Proceedings of ICDIS 2023
A2 - Tiwari, Shailesh
A2 - Trivedi, Munesh C.
A2 - Kolhe, Mohan L.
A2 - Singh, Brajesh Kumar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Data and Information Sciences, ICDIS 2023
Y2 - 16 June 2023 through 17 June 2023
ER -