A Scheme on Object Tracking Techniques in Multiple CCTV IoT Environments (다중 CCTV 사물인터넷 환경에서의 객체 추적 기법)

Ji-Hoon Hong (홍지훈); Keun-Ho Lee (이근호)

A Scheme on Object Tracking Techniques in Multiple CCTV IoT Environments

Journal of Internet of Things and Convergence
Abbr : JKIOTS
2019, 5(1), pp.7-11
Publisher : The Korea Internet of Things Society
Research Area : Engineering > Computer Science > Internet Information Processing
Received : February 10, 2019
Accepted : March 29, 2019
Published : June 30, 2019

Ji-Hoon Hong ¹, Keun-Ho Lee ¹

¹백석대학교

Candidate

ABSTRACT

This study suggests a methodology to track crime suspects or anomalies through CCTV in order to expand the scope of CCTV use as the number of CCTV installations continues to increase nationwide in recent years. For the abnormal behavior classification, we use the existing studies to find out suspected criminals or abnormal actors, use CNN to track objects, and connect the surrounding CCTVs to each other to predict the movement path of objectified objects CCTVs in the vicinity of the path were used to share objects' sample data to track objects and to track objects. Through this research, we will keep track of criminals who can not be traced, contribute to the national security, and continue to study them so that more diverse technologies can be applied to CCTV.

KEYWORDS

Physical security, artificial intelligence, CNN algorithm, object detection, CCTV

Citation status

* References for papers published after 2023 are currently being built.

[journal] Y.K.Son / 2003 / Practical use plan of equipment security system for efficient crime prevention (CCTV system in priority / KOREAN INSTITUTE OF FIRE SCIENCE & ENGINEERING : 393~399

[journal] 한재경 / 2018 / A Study on Establishment and Management of the Crime Prevention CCTV / 공공사회연구 / 한국공공사회학회 8(4) : 109~137

[journal] 허선영 / 2015 / An Analysis on the CCTV Location Appropriateness and Effectiveness for the Crime Prevention / 한국지역지리학회지 / 한국지역지리학회 21(4) : 739~750

[journal] D.W.Kim / 2000 / The Implementation of the structure and algorithm of Fuzzy Self-organizing Neural Networks(FSONN) based on CNN / Korean Institute of Intelligent Systems 10(1) : 114~117

[journal] B.J.Park / 2000 / The Hybrid Multi-layer Inference Architectures and Algorithms of FPNN Based on CNN and PNN / The Korean Institute of Electrical Engineers 49(7) : 378~388

[journal] J.H.Kim / 2018 / Shadow Detection for the Accuracy of Object Detection in CCTV Image / Korean Institute of Information Technology : 191~193

[journal] S.H.Lee / 2018 / Design of Efficient Object Detection System Using Object Recognition Technology / The Institute of Electronics and Information Engineers : 829~831

[journal] S.K.Kim / 2018 / Connectivity Based Object Tracking Model for Intelligent Video Surveillance / The Korean Institute of Electrical Engineers : 1131~1132

[journal] T.Y.Nam / 2016 / Design of KLT Tracker for Real-time Object Detection and Tracking using System-on-Chip / The Korean Society for Aeronautical & Space Sciences : 805~807

[web] Jonathan. Hui / 2018 / Object detection: speed and accuracy comparison (Faster R-CNN, R-FCN, SSD, FPN, RetinaNet and YOLOv3) / https://medium.com/@jonathan_hui/object-detection-speed-and-accuracy-comparison-faster-r-cnn-r-fcn-ssd-and-yolo-5425656ae359

[journal] J.H.Elder / 2007 / Pre-Attoentive and Attentive Detection of Humans in Wide-Filed Scence / Intematinal Journal of Computer 72(1) : 47~66

[journal] 이우주 / 2009 / Multiple Object Detection and Tracking System robust to various Environment / 전자공학회논문지 - SP / 대한전자공학회 46(6) : 88~94

[journal] J.W.Park / 2014 / Crowed abnormal behaviors detection for video surveillance systems / Korea Institute Of Communication Sciences : 376~377

[journal] Y.J.Jung / 2014 / A Study on Abnormal Behavior Analysis and Pattern Prediction using Multi-object / The Korean Institute of Information Scientists and Engineers : 440~441

[journal] 이승원 / 2011 / Abnormal Behavior Detection Based on Adaptive Background Generation for Intelligent Video Analysis / 전자공학회논문지 - SP / 대한전자공학회 48(1) : 111~121

KJCKorea
Journal Central

Journal of Internet of Things and Convergence 2023 KCI Impact Factor : 0.89