Visual Object Tracking using Deep Learning

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