[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126016-en":3,"doc-seo-126016-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126016,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","DEVELOPMENT OF AN INTELLIGENT SURVEILLANCE SYSTEM FOR GESTURE RECOGNITION USING MACHINE LEARNING","Video surveillance is widely used in traffic control, crowd control, and wildlife protection, yet manual monitoring remains manpower intensive and vulnerable to compromise. With growing security demands and increasing costs from mass data storage, intelligent video monitoring becomes essential for improving safety in public environments. This research presents a gesture-recognition intelligent surveillance system combining computer vision, MediaPipe landmark extraction, and machine learning classifiers to distinguish normal and anomalous gestures and support timely alerts.","LAUTECH Journal of Engineering and Technology 18 (1) 2024: 173-180  \nDEVELOPMENT OF AN INTELLIGENT SURVEILLANCE SYSTEM FOR GESTURE RECOGNITION USING MACHINE LEARNING  \nOmodunbi B. A., *Soladoye A. A., Adeyanju I. A. and Adeyo S. O.  \nDepartment of Computer Engineering, Federal University Oye-Ekiti. Nigeria.  \n[Corresponding author:](Corresponding author: afeez.soladoye@fuoye.edu.ng)[ ](Corresponding author: afeez.soladoye@fuoye.edu.ng)[afeez.soladoye@fuoye.edu.ng](Corresponding author: afeez.soladoye@fuoye.edu.ng)  \nABSTRACT  \nOver the years, video surveillance systems have been in use in various contexts such as traffic control, crowd control and protecting wildlife. In a dispensation characterized by security concerns and technological advancements, it has become necessary to develop intelligent systems for surveillance. Intelligent video monitoring has become a vital tool for boosting security and safety in public areas. These systems combine the use of computer vision, machine learning, and artificial intelligence techniques to analyse video data and alert security personnel to potential threats. However, traditional manual monitoring methods need a lot of manpower and are easily compromised. In addition, the cost of video surveillance goes up with mass data storage. This research proposes an Intelligent Surveillance System for Gesture Recognition using machine learning techniques. Video data showing normaly and anomaly gestures were captured using a phone camera connected to a personal computer (PC) using Iriun Webcam. The coordinates of this and landmarks obtained from these videos were gathered using media pipe showing different gesture classes. The hold-out evaluation method was employed with 70-30% split. The acquired videos were trained for gesture recognition using four different machine learning pipelines namely: linear regression, Ridge Classifier, Random Forest Classifier, and Gradient Boosting Classifier. Ridge classifier gave the best average accuracy, precision and F1 score of 99.8, 99.8 and 99.4% respectively when evaluated. This study shows that Ridge classifier provides a good classifier for gesture recognition using video coordinates and landmarks.  \nKeywords: Gesture recognition, Surveillance, Machine Learning, Ridge classifier.  \nINTRODUCTION  \nIntelligent video surveillance makes use of advanced technologies like artificial intelligence (AI), machine learning (ML), and computer vision to automatically monitor and analyse video feeds from security cameras. These technologies are used by intelligent video surveillance systems to detect, recognize, and track objects, people, and vehicles in real time. They can also automatically warn and notify security professionals in the event of any suspicious or unusual behaviour. Recognition of human activities is a recent field that is intended to provide techniques and methods allowing the detection and classification of human activities and extended now to recognize normal or abnormal  \nactivities (Neha et al., 2022) . Video surveillance systems can now identify potential dangers and unusual activities in real-time courtesy of the development of high-resolution cameras and sophisticated video analytics. However, the efficiency of these systems is somewhat constrained by some issues. The first issue is that current systems frequently have high false alarm rates, which can cause security staff to become overworked and unable to react to real threats. Second, video surveillance systems must balance the need for security with each individual's right to privacy, generating ethical issues and creating legal challenges. In this research, we employed  \nMediaPipe for gesture recognition using machine learning.  \nMediaPipe is an open-source framework created by Google. It offers a comprehensive and adaptable selection of machine learning solutions for diverse multimedia processing applications, including computer vision and media understanding. It is used to attain estimate","cbCaicSqIaLXe903","https://ap.wps.com/l/cbCaicSqIaLXe903","pdf",1199443,5,1,"English","en",105,"# Abstract\n# Introduction\n## Intelligent video surveillance and its challenges\n## Gesture recognition with MediaPipe\n## Machine learning fundamentals","[{\"question\":\"What problem does the proposed system address compared with traditional video monitoring?\",\"answer\":\"Traditional manual monitoring requires many staff members and is easily compromised, while the proposed approach uses intelligent analysis to improve responsiveness and reduce reliance on manpower.\"},{\"question\":\"How are gesture data and features obtained in this study?\",\"answer\":\"Normal and anomalous gesture videos are captured with a phone camera, then MediaPipe is used to extract landmarks and coordinates for different gesture classes.\"},{\"question\":\"Which machine learning pipeline performed best for gesture recognition?\",\"answer\":\"The Ridge Classifier achieved the highest average accuracy, precision, and F1 score (99.8, 99.8, and 99.4% respectively) in hold-out evaluation with a 70-30 split.\"}]","DEVELOPMENT OF AN INTELLIGENT SURVEILLANCE SYSTEM FOR GESTURE RECOGNITION USING MACHINE LEARNING | 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problem does the proposed system address compared with traditional video monitoring?","Question",{"text":76,"@type":77},"Traditional manual monitoring requires many staff members and is easily compromised, while the proposed approach uses intelligent analysis to improve responsiveness and reduce reliance on manpower.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are gesture data and features obtained in this study?",{"text":81,"@type":77},"Normal and anomalous gesture videos are captured with a phone camera, then MediaPipe is used to extract landmarks and coordinates for different gesture classes.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning pipeline performed best for gesture recognition?",{"text":85,"@type":77},"The Ridge Classifier achieved the highest average accuracy, precision, and F1 score (99.8, 99.8, and 99.4% respectively) in hold-out evaluation with a 70-30 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