Visual Object Tracking using Deep Learning

This book covers the description of both conventional methods and advanced methods. In conventional methods, visual tracking techniques such as stochastic, deterministic, generative, and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In advanced methods, various categories of deep learning-based trackers and correlation filter-based trackers are analyzed. The book also: Discusses potential performance metrics used for comparing the efficiency and effectiveness of various visual tracking methods Elaborates on the salient features of deep learning trackers along with traditional trackers, wherein the handcrafted features are fused to reduce computational complexity Illustrates various categories of correlation filter-based trackers suitable for superior and efficient performance under tedious tracking scenarios Explores the future research directions for visual tracking by analyzing the real-time applications The book comprehensively discusses various deep learning-based tracking architectures along with conventional tracking methods. It covers in-depth analysis of various feature extraction techniques, evaluation metrics and benchmark available for performance evaluation of tracking frameworks. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

Table of Contents:
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