Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time
作者机构：Department of Emergent ComputingSchool of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Malaysia-Japan International Institute of Technology(MJIIT)Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia Media and Game Innovation Centre of Excellence(MaGICX)Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Engineering TechnologyUniversity of MemphisMemphisTN38152USA Department of Computer Science and Artificial IntelligenceCollege of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Information System and Cyber SecurityCollege of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia
出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)
年 卷 期：2024年第39卷第3期
页 面：417-436页
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)
摘 要：Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance *** scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and *** research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image *** annotated image dataset trains the proposed system and predicts the camera pose in *** system mainly improved the localization performance of indoor cameras by more accurately predicting the camera *** also recognizes the scene changes during the sequence and evaluates the effects of these *** system achieved high accuracy and real-time *** scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact ***,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.
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