An implementation of intelligent YOLOv3-based anomaly detection model from crowded video scenarios with optimized ensemble pattern extraction

ABSTRACTThe anomaly or abnormality detection in crowded scenes helps in identifying the violence and protecting the people from severe damage. Thus, there is a need to detect the anomalies with the classifier for learning information along with the usage of huge architectures. A new anomaly detection model is implemented in this model. The collected data is fed to optimal ensemble pattern extraction scheme through techniques like Local binary patterns (LBP), Local Gradient Pattern (LGP), and Local Tetra Pattern (LTrP). The weights are tuned by a new hybrid Spiral Search-based Black Widow Glowworm Swarm Optimization (SS-BWGSO) for getting the optimal ensemble patterns. Next, anomaly frame classification is carried out by optimized VGG16+ResNet technique, where the hyperparameters of VGG16 and ResNet are tuned by SS-BWGSO algorithm. Finally, anomaly detection is performed by the YOLOV3 classifier. Throughout the result analysis the higher performance of the designed technique is observed over the classical methods.KEYWORDS: Anomaly detectioncrowded video scenariosoptimized ensemble pattern extractionspiral search-based black widow glowworm swarm optimizationoptimized VGG16 and ResNetYOLOv3 Disclosure statementNo potential conflict of interest was reported by the author(s).Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Additional informationNotes on contributorsPoorni R.Poorni Ramakrishnan, with an experience of around 10 years in Teaching, Ms. R. Poorni is currently working at Sri Venkateswara College of Engineering, Sriperumbudur as an Assistant Professor in the Department of Computer Science & Engineering. Ms. R. Poorni received her B.E from Meenakshi College of Engineering, Anna University, Chennai, and M.E from SKR Engineering College, Anna University, Chennai and pursuing Ph.D. in SRM Institute of Science and Technology, Kattankulathur, Chennai. She was a meritorious candidate during her entire course of studies. She was a rank holder in her PG studies. The research area includes the field of computer vision and video analytics. She has published more than 8 papers in various international journals and international conferences. She has applied for various funded proposals and has also received fund from TNSCST to complete her project in smart contract-based medical supply chain management. She has also published two patent under IPR, India.Madhavan P.Dr. P. Madhavan obtained his Bachelor's Degree-B.E. in Electronics and Communication Engineering and Master's Degree-M.E. in Multimedia Technology. He has completed his Ph.D. degree from Anna University, Chennai in the field of Wireless Adhoc Network. He started his carrier as Lecturer in the year 2002. At present, He is working as Associate Professor at SRM University, Chennai. He has more than 13 years of teaching experience in Engineering College. He has published more than 20 papers in International and National Journals, 30 International and National Conferences. He is a Life member of Indian Society for Technical Education (ISTE), International Association of Engineers (IAENG). His areas of Interest include Wireless adhoc and sensor networks, Multimedia Communication, Fuzzy and Neural Computing.