Visual Object Tracking using Deep Learning by Ashish Kumar Paperback Book

The Nile on
Visual Object Tracking using Deep Learning by Ashish Kumar

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.

FORMAT
Paperback CONDITION
Brand New Publisher Description

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 sequences1.1 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 SummaryChapter 2
Background and research orientation for visual tracking appearance model: Standards and Models2.1 Background and preliminaries
2.2 Conventional tracking methods
2.3 Deep learning-based methods
2.4 Correlation filter based visual trackers
2.5 SummaryChapter 3
Target feature extraction for robust appearance model3.1. 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 SummaryChapter 4
Performance metrics for visual tracking: A Qualitative and Quantitative analysis4.1 Introduction
4.2 Performance metrics for tracker evaluation
4.3 Performance metrics without ground truth
4.4 Performance metrics with ground truth
4.5 SummaryChapter 5
Visual tracking datasets: Benchmark for Evaluation5.1 Introduction
5.2 Problem with the self-generated datasets
5.3 Salient features of visual tracking public datasetsChapter 6Conventional framework for visual tracking: Challenges and solutions6.1 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 SummaryChapter 7Stochastic 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 SummaryChapter 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 SummaryChapter 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 SummaryChapter 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 SummaryChapter 11
Future prospects of visual tracking: Application Specific Analysis11.1 Introduction
11.2 Pruning for deep neural architecture
11.3 Explainable AI
11.4 Application-specific visual tracking
11.6 SummaryChapter 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

Details ISBN1032598077 Author Ashish Kumar Pages 202 Publisher Taylor & Francis Ltd Year 2025 ISBN-13 9781032598079 Format Paperback Imprint CRC Press Place of Publication London Country of Publication United Kingdom Alternative 9781032490533 DEWEY 005.13 Illustrations 31 Tables, black and white; 10 Line drawings, black and white; 9 Halftones, black and white; 15 Illustrations, color; 4 Illustrations, black and white Audience Tertiary & Higher Education ISBN-10 1032598077 Country of Origin GB Product Class Description Engineering & Technology: Textbooks & Study Guides Publication Date 2025-06-26 UK Release Date 2025-06-27
We\'ve got this

At The Nile, if you\'re looking for it, we\'ve got it.
With fast shipping, low prices, friendly service and well over a million items - you\'re bound to find what you want, at a price you\'ll love!

TheNile_Item_ID:176547933;