[ad_1]

Introduction

In recent years, the field of computer vision has seen significant advancements, particularly in the area of human action recognition. The ability to automatically recognize and classify human actions in videos has a wide range of applications, including surveillance, human-computer interaction, and sports analysis. Deep learning, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has emerged as a powerful tool for tackling complex video analysis tasks.

This thesis aims to develop a deep learning-based system for video-based human action recognition. The system will utilize state-of-the-art deep learning techniques to accurately classify and recognize human actions in videos. By leveraging the capabilities of deep learning, we aim to overcome the challenges associated with traditional hand-crafted feature extraction methods and improve the performance of human action recognition systems.

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
– Overview of human action recognition
– Traditional methods for human action recognition
– Deep learning approaches for video-based action recognition
– State-of-the-art deep learning models for video analysis
– Challenges and limitations of current systems
– Applications of human action recognition technology
– Comparison of different deep learning architectures for video analysis
– Transfer learning techniques in human action recognition
– Performance evaluation metrics in action recognition
– Recent advancements in deep learning for video analysis

Chapter 3: Research Methodology
– Data collection and preprocessing
– Feature extraction techniques
– Model architecture design
– Training and optimization methods
– Evaluation metrics
– Experimental setup
– Cross-validation techniques
– Ethical considerations in data collection

Chapter 4: Discussion of Findings
– Performance evaluation of the developed system
– Comparison with state-of-the-art methods
– Analysis of results
– Discussion on the effectiveness of deep learning in action recognition
– Limitations and potential improvements
– Insights for future research

Chapter 5: Conclusion and Summary
– Summary of key findings
– Contributions of the study
– Implications of the research
– Future directions for research
– Conclusion and recommendations for practice

Thesis Overview

The advancement of deep learning techniques has revolutionized the field of computer vision, particularly in the area of human action recognition. This thesis aims to develop a deep learning-based system for video-based human action recognition. By leveraging the capabilities of deep learning, we strive to overcome the limitations of traditional hand-crafted feature extraction methods and improve the accuracy and efficiency of human action recognition systems.

In Chapter 1, we provide an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive review of the existing literature on human action recognition, traditional and deep learning approaches to video analysis, state-of-the-art deep learning models, challenges faced by current systems, and recent advancements in the field.

Chapter 3 outlines the research methodology, including data collection and preprocessing, feature extraction techniques, model architecture design, training and optimization methods, evaluation metrics, experimental setup, cross-validation techniques, and ethical considerations. In Chapter 4, we discuss the findings of the study, including the performance evaluation of the developed system, comparisons with existing methods, analysis of results, limitations, potential improvements, and insights for future research.

Finally, Chapter 5 presents the conclusion and summary of the thesis, highlighting the key findings, contributions of the study, implications for practice, future research directions, and recommendations. By developing a deep learning-based system for video-based human action recognition, this thesis aims to contribute to the advancement of computer vision technology and its applications in various domains.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.