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Introduction

Action recognition is a crucial task in video understanding and has gained significant attention in recent years due to its wide range of applications in various fields such as surveillance, human-computer interaction, sports analysis, and healthcare. The ability to recognize and interpret human actions in videos can provide valuable insights for automated systems to make informed decisions and improve user experiences.

This thesis aims to explore different approaches and techniques for action recognition in videos, with a focus on deep learning models and computer vision algorithms. By leveraging the power of artificial intelligence and machine learning, we aim to develop a robust and accurate system for recognizing and classifying human actions in videos.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of Action Recognition
2.2 Traditional Approaches to Action Recognition
2.3 Deep Learning for Action Recognition
2.4 Convolutional Neural Networks (CNNs)
2.5 Recurrent Neural Networks (RNNs)
2.6 Spatio-temporal Features Extraction
2.7 Temporal Convolutional Networks (TCNs)
2.8 Two-stream Networks
2.9 Attention Mechanisms
2.10 Transfer Learning in Action Recognition

Chapter 3: System Design and Methodology
3.1 Data Collection and Preprocessing
3.2 Feature Extraction
3.3 Model Selection
3.4 Training and Evaluation
3.5 Hyperparameter Tuning
3.6 Ensemble Learning
3.7 Fine-tuning Models
3.8 Performance Evaluation Metrics

Chapter 4: System Implementation
4.1 Implementation Environment
4.2 Dataset Selection
4.3 Data Augmentation Techniques
4.4 Model Architecture
4.5 Training Process
4.6 Model Evaluation
4.7 Results Analysis
4.8 Comparison with Existing Approaches

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Future Directions
5.4 Conclusion

Thesis Overview: Action recognition for video understanding

Action recognition is an important task in the field of computer vision, with applications ranging from surveillance to sports analysis. This thesis focuses on exploring different approaches and techniques for action recognition in videos, with a specific emphasis on deep learning models and computer vision algorithms. By leveraging the power of artificial intelligence and machine learning, we aim to develop a robust and accurate system for recognizing and classifying human actions in videos.

Chapter 1 provides an introduction to the topic, outlining the background, problem statement, objectives, limitations, and scope of the study. It also discusses the significance of the study and provides the structure of the thesis along with definitions of key terms.

Chapter 2 presents a comprehensive literature review on action recognition, covering traditional approaches, deep learning techniques, convolutional neural networks, recurrent neural networks, spatio-temporal feature extraction, temporal convolutional networks, two-stream networks, attention mechanisms, and transfer learning.

Chapter 3 delves into the system design and methodology, discussing data collection and preprocessing, feature extraction, model selection, training and evaluation, hyperparameter tuning, ensemble learning, fine-tuning models, and performance evaluation metrics.

Chapter 4 details the system implementation, including the implementation environment, dataset selection, data augmentation techniques, model architecture, training process, model evaluation, results analysis, and comparison with existing approaches.

Chapter 5 concludes the thesis by summarizing the findings, highlighting the contributions of the study, outlining future directions, and providing a conclusion on the overall project. The thesis aims to contribute to the field of action recognition for video understanding and provide insights for further research and development in this area.

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