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001481293 001__ 1481293
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001481293 005__ 20231031003331.0
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001481293 019__ $$a1400013826
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001481293 0247_ $$a10.1007/978-981-99-3288-7$$2doi
001481293 035__ $$aSP(OCoLC)1401059016
001481293 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE
001481293 049__ $$aISEA
001481293 050_4 $$aTA1634
001481293 08204 $$a006.37$$223/eng/20231013
001481293 24500 $$aObject tracking technology :$$btrends, challenges and applications /$$cAshish Kumar, Rachna Jain, Ajantha Devi Vairamani, Anand Nayyar, editors.
001481293 260__ $$aSingapore :$$bSpringer,$$c2023.
001481293 300__ $$a1 online resource (280 p.).
001481293 4901_ $$aContributions to Environmental Sciences and Innovative Business Technology
001481293 500__ $$a3 Deep Learning Framework for Detecting Video Anomalies in a Multimodal Semi-supervised Environment
001481293 5050_ $$aIntro -- Preface -- Contents -- About the Editors -- Single-Object Detection from Video Streaming -- 1 Introduction -- 2 Related Work -- 2.1 Two-Stage-Based Object Detection -- 2.2 One-Stage-Based Object Detection -- 3 Materials and Methods -- 3.1 Materials -- 3.1.1 Dataset -- 3.1.2 Data Pre-processing -- 3.1.3 Data Augmentation -- 3.1.4 Data Annotation -- 3.2 Methods -- 3.2.1 Overview of YOLOv4 -- 4 Applications of YOLOv4 -- 5 Proposed Methodology -- 6 Experimentation and Implementation -- 6.1 Experimental Setup -- 6.2 Training YOLOv4 -- 6.3 Evaluation Measures
001481293 5058_ $$a7 Results and Performance Analysis -- 8 Conclusion and Future Work -- References -- Different Approaches to Background Subtraction and Object Tracking in Video Streams: A Review -- 1 Introduction -- 2 Literature Review -- 2.1 Survey on Frame Rate Conversion Techniques -- 2.2 Survey on Foreground Extraction Techniques -- 2.3 Feature Extraction Methods -- 2.4 Machine Learning Approaches for Pedestrian Detection -- 2.5 Deep Learning Approaches for Pedestrian Detection -- 3 Conclusion and Future Scope -- References
001481293 5058_ $$aAuto Alignment of Tanker Loading Arm Utilizing Stereo Vision Video and 3D Euclidean Scene Reconstruction -- 1 Introduction -- 2 State of Research in the Field -- 2.1 Stereo View Geometry -- 2.2 Geometry of an Epipolar Camera -- 2.3 Marine Loading Arms -- 3 Research Methodology -- 3.1 Disparity Map -- 3.2 Calibration -- 3.3 Feature Recognition -- 3.4 Calibration and Model Fitting -- 3.5 Distance Calculation -- 3.6 Reconstruction Error -- 4 Experimentation and Results -- 4.1 Extraction of a Specific Target -- 4.2 Results of Calibration -- 4.3 Reconstruction Error
001481293 5058_ $$a4.4 Effects of Errors on 3D Reconstruction -- 5 Conclusion and Future Scope -- References -- Visual Object Segmentation Improvement Using Deep Convolutional Neural Networks -- 1 Introduction -- 2 Methods for Image Retrieval -- 2.1 Image Retrieval Techniques -- 3 Literature Review -- 4 Data Analysis of Visual Object Segmentation -- 5 Pre-processing for Anatomical MRI Source Estimation -- 5.1 Pre-processing for Anatomical Parcellation Labels -- 6 Source Activity Reconstruction and Encoding Model -- 7 Nested Cross-Validation and Encoding Linear Model
001481293 5058_ $$a8 Encoding and Decoding of Pixel Space Control Model -- 8.1 Improved Cascade Mask R-CNN Model -- 9 Conclusion and Future Scope -- References -- Applications of Deep Learning-Based Methods on Surveillance Video Stream by Tracking Various Suspicious Activities -- 1 Introduction -- 2 Methods for Detecting Video Anomalies Based on Deep Learning -- 2.1 Using Patterns as a Global Framework -- 2.2 Methods Based on Grid Patterns -- 2.3 Learning Models Based on Representations -- 2.4 Discriminative Models -- 2.5 Models of Deep One-Class Categorization -- 2.6 Models with Deep Hybridization
001481293 506__ $$aAccess limited to authorized users.
001481293 520__ $$aWith the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In contrast to single object tracking, multi-target introduces one extra step of detection. Tracking multi-target includes detecting and categorizing the target into multiple classes in the first frame and provides each individual target an ID to keep its track in the subsequent frames of a video stream. One category of multi-target algorithms exploits global information to track the target of the detected target. On the other hand, some algorithms consider present and past information of the target to provide efficient tracking solutions. Apart from these, deep leaning-based algorithms provide reliable and accurate solutions. But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain. The various feature that can be extracted from the target and target saliency prediction is also covered. It explores a comprehensive analysis of the evolution from traditional methods to deep learning methods, from single object tracking to multi-target tracking. In addition, the application of visual tracking and the future of visual tracking can also be introduced to provide the future aspects in the domain to the reader. This book also discusses the advancement in the area with critical performance analysis of each proposed algorithm. This book will be formulated with intent to uncover the challenges and possibilities of efficient and effective tracking of single or multi-object, addressing the various environmental and hardware challenges. The intended audience includes academicians, engineers, postgraduate students, developers, professionals, military personals, scientists, data analysts, practitioners, and people who are interested in exploring more about tracking. Another projected audience are the researchers and academicians who identify and develop methodologies, frameworks, tools, and applications through reference citations, literature reviews, quantitative/qualitative results, and discussions.
001481293 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 13, 2023).
001481293 650_0 $$aComputer vision.$$vCongresses$$0(DLC)sh2008101162
001481293 655_0 $$aElectronic books.
001481293 7001_ $$aKumar, Ashish$$c(Professor of computer science)
001481293 7001_ $$aJain, Rachna$$c(Professor of information technology)
001481293 7001_ $$aDevi, V. Ajantha,$$q(Vairamani Ajantha),$$d1981-
001481293 7001_ $$aNayyar, Anand.
001481293 77608 $$iPrint version:$$aKumar, Ashish$$tObject Tracking Technology$$dSingapore : Springer,c2023$$z9789819932870
001481293 830_0 $$aContributions to Environmental Sciences and Innovative Business Technology.
001481293 852__ $$bebk
001481293 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-3288-7$$zOnline Access$$91397441.1
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001481293 980__ $$aBIB
001481293 980__ $$aEBOOK
001481293 982__ $$aEbook
001481293 983__ $$aOnline
001481293 994__ $$a92$$bISE