The text comprehensively discusses tracking architecture under stochastic and deterministic frameworks and presents experimental results under each framework with qualitative and quantitative analysis. It covers deep learning techniques for feature extraction, template matching, and training the networks in tracking algorithms.
The text comprehensively discusses tracking architecture under stochastic and deterministic frameworks and presents experimental results under each framework with qualitative and quantitative analysis. It covers deep learning techniques for feature extraction, template matching, and training the networks in tracking algorithms.
Dr. Ashish Kumar, Ph.D., is working as an assistant professor with Bennett University, Greater Noida, U.P., India. He has completed his Ph.D. in Computer Science and Engineering from Delhi Technological University (formerly DCE), New Delhi, India in 2020. He has received best researcher award from the Delhi Technological University for his contribution in the computer vision domain. He has completed M.Tech with distinction in computer Science and Engineering from GGS Inderpratha University, New Delhi. He has published many research papers in various reputed national and international journals and conferences. His current research interests include object tracking, image processing, artificial intelligence, and medical imaging analysis.
Inhaltsangabe
Chapter 1 Introduction to visual tracking in video sequences 1.1 Overview of visual tracking in video sequences 1.2 Motivation and challenges 1.3 Real-time applications of visual tracking 1.4 Emergence from the conventional to deep learning approaches 1.5 Performance evaluation criteria 1.6 Summary Chapter 2 Background and research orientation for visual tracking appearance model: Standards and Models 2.1 Background and preliminaries 2.2 Conventional tracking methods 2.3 Deep learning-based methods 2.4 Correlation filter based visual trackers 2.5 Summary Chapter 3 Target feature extraction for robust appearance model 3.1. Saliency feature extraction for visual tracking 3.2 Handcrafted features 3.3 Deep learning for feature extraction 3.4 Multi-feature fusion for efficient tracking 3.5 Summary Chapter 4 Performance metrics for visual tracking: A Qualitative and Quantitative analysis 4.1 Introduction 4.2 Performance metrics for tracker evaluation 4.3 Performance metrics without ground truth 4.4 Performance metrics with ground truth 4.5 Summary Chapter 5 Visual tracking datasets: Benchmark for Evaluation 5.1 Introduction 5.2 Problem with the self-generated datasets 5.3 Salient features of visual tracking public datasets Chapter 6 Conventional framework for visual tracking: Challenges and solutions 6.1 Introduction 6.2 Deterministic tracking approach 6.2.1 Meanshift and its variant-based trackers 6.2.2 Multi-modal deterministic approach 6.3 Generative tracking approach 6.4 Discriminative tracking approach 6.5 Summary Chapter 7 Stochastic framework for visual tracking: Challenges and Solutions 7.1 Introduction 7.2 Particle filter for visual tracking 7.3 Framework and procedure 7.4 Fusion of multi-feature and State estimation 7.5 Experimental Validation of the particle filter based tracker 7.6 Discussion on PF-variants based tracking 7.7 Summary Chapter 8 Multi-stage and collaborative framework for visual tracking 8.1 Introduction 8.2 Multi-stage tracking algorithms 8.3 Framework and procedures 8.4 Collaborative tracking algorithms 8.5 Summary Chapter 9 Deep learning based visual tracking model: A paradigm shift 9.1 Introduction 9.2 Deep learning-based tracking framework 9.3 Hyper-feature based deep learning networks 9.4 Multi-modal based deep learning trackers 9.5 Summary Chapter 10 Correlation filter-based visual tracking model: Emergence and upgradation 10.1 Introduction 10.2 Correlation filter-based tracking framework 10.3 Deep Correlation Filter based trackers 10.4 Fusion-based correlation filter trackers 10.5 Discussion on correlation filter-based trackers 10.6 Summary Chapter 11 Future prospects of visual tracking: Application Specific Analysis 11.1 Introduction 11.2 Pruning for deep neural architecture 11.3 Explainable AI 11.4 Application-specific visual tracking 11.6 Summary Chapter 12 Deep learning-based multi-object tracking: Advancement for intelligent video analysis 12.1 Introduction 12.2 Multi-object tracking algorithms 12.3 Evaluation metrics for performance analysis 12.4 Benchmark for performance evaluation 12.5 Application of MOT algorithms 12.6 Limitations of existing MOT algorithms 12.7 Summary
Chapter 1 Introduction to visual tracking in video sequences 1.1 Overview of visual tracking in video sequences 1.2 Motivation and challenges 1.3 Real-time applications of visual tracking 1.4 Emergence from the conventional to deep learning approaches 1.5 Performance evaluation criteria 1.6 Summary Chapter 2 Background and research orientation for visual tracking appearance model: Standards and Models 2.1 Background and preliminaries 2.2 Conventional tracking methods 2.3 Deep learning-based methods 2.4 Correlation filter based visual trackers 2.5 Summary Chapter 3 Target feature extraction for robust appearance model 3.1. Saliency feature extraction for visual tracking 3.2 Handcrafted features 3.3 Deep learning for feature extraction 3.4 Multi-feature fusion for efficient tracking 3.5 Summary Chapter 4 Performance metrics for visual tracking: A Qualitative and Quantitative analysis 4.1 Introduction 4.2 Performance metrics for tracker evaluation 4.3 Performance metrics without ground truth 4.4 Performance metrics with ground truth 4.5 Summary Chapter 5 Visual tracking datasets: Benchmark for Evaluation 5.1 Introduction 5.2 Problem with the self-generated datasets 5.3 Salient features of visual tracking public datasets Chapter 6 Conventional framework for visual tracking: Challenges and solutions 6.1 Introduction 6.2 Deterministic tracking approach 6.2.1 Meanshift and its variant-based trackers 6.2.2 Multi-modal deterministic approach 6.3 Generative tracking approach 6.4 Discriminative tracking approach 6.5 Summary Chapter 7 Stochastic framework for visual tracking: Challenges and Solutions 7.1 Introduction 7.2 Particle filter for visual tracking 7.3 Framework and procedure 7.4 Fusion of multi-feature and State estimation 7.5 Experimental Validation of the particle filter based tracker 7.6 Discussion on PF-variants based tracking 7.7 Summary Chapter 8 Multi-stage and collaborative framework for visual tracking 8.1 Introduction 8.2 Multi-stage tracking algorithms 8.3 Framework and procedures 8.4 Collaborative tracking algorithms 8.5 Summary Chapter 9 Deep learning based visual tracking model: A paradigm shift 9.1 Introduction 9.2 Deep learning-based tracking framework 9.3 Hyper-feature based deep learning networks 9.4 Multi-modal based deep learning trackers 9.5 Summary Chapter 10 Correlation filter-based visual tracking model: Emergence and upgradation 10.1 Introduction 10.2 Correlation filter-based tracking framework 10.3 Deep Correlation Filter based trackers 10.4 Fusion-based correlation filter trackers 10.5 Discussion on correlation filter-based trackers 10.6 Summary Chapter 11 Future prospects of visual tracking: Application Specific Analysis 11.1 Introduction 11.2 Pruning for deep neural architecture 11.3 Explainable AI 11.4 Application-specific visual tracking 11.6 Summary Chapter 12 Deep learning-based multi-object tracking: Advancement for intelligent video analysis 12.1 Introduction 12.2 Multi-object tracking algorithms 12.3 Evaluation metrics for performance analysis 12.4 Benchmark for performance evaluation 12.5 Application of MOT algorithms 12.6 Limitations of existing MOT algorithms 12.7 Summary
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Shop der buecher.de GmbH & Co. KG Bürgermeister-Wegele-Str. 12, 86167 Augsburg Amtsgericht Augsburg HRA 13309
