Moving Object Detection Using Background Subtraction Algorithms

Inhaltsverzeichnis (Table of Contents)

• ABSTRACT
• LIST OF FIGURES
• LIST OF TABLES
• CHAPTER 1: INTRODUCTION
• 1.1 Introduction
• 1.2 Problem Definition
• 1.3 Motivation
• 1.4 Aim and Objectives
• 1.5 Scope and Organization of the Dissertation
• CHAPTER 2: LITERATURE REVIEW
• 2.1 Introduction
• 2.2 History of Object Detection
• 2.3 Various Techniques for Object Detection & Tracking
• 2.4 Object Detection in Video Surveillance
• 2.5 Applications of Object Detection
• 2.6 Challenges in Object Detection
• CHAPTER 3: BACKGROUND SUBTRACTION ALGORITHMS
• 3.1 Introduction
• 3.2 Background Subtraction: Techniques
• 3.3 Simple Background Subtraction
• 3.4 Statistical Methods
• 3.5 Frame Differencing
• 3.6 W4 System
• 3.7 Gaussian Mixture Model
• 3.8 Eigenbackground Model
• CHAPTER 4: EXPERIMENTAL RESULTS & ANALYSIS
• 4.1 Introduction
• 4.2 Performance Evaluation Metrics
• 4.3 Experimental Results
• CHAPTER 5: OPTICAL FLOW BASED MOVING OBJECT DETECTION
• 5.1 Introduction
• 5.2 Optical Flow Techniques
• 5.3 Horn-Schunck Algorithm for Optical Flow
• 5.4 Implementation of Horn-Schunck Algorithm
• CHAPTER 6: COMBINED APPROACH FOR MOVING OBJECT DETECTION
• 6.1 Introduction
• 6.2 Combining Background Subtraction and Optical Flow
• 6.3 Implementation and Experimental Results
• CHAPTER 7: SHADOW DETECTION & REMOVAL
• 7.1 Introduction
• 7.2 Shadow Detection Techniques
• 7.3 Implementation and Experimental Results

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This dissertation focuses on developing a practical computer vision system for detecting and tracking moving objects in video sequences. The main objective is to explore and implement various background subtraction algorithms and techniques for efficient and accurate moving object segmentation.

• Background Subtraction Algorithms
• Moving Object Detection and Tracking
• Video Surveillance and Security
• Computer Vision and Image Processing
• Performance Evaluation and Comparison

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1: Introduction This chapter introduces the concept of moving object detection and tracking, outlining the problem, motivation, and goals of the dissertation. It also defines the scope and structure of the research.
• Chapter 2: Literature Review This chapter presents a comprehensive overview of existing techniques for object detection and tracking, highlighting relevant research works, challenges, and applications in video surveillance and other fields.
• Chapter 3: Background Subtraction Algorithms This chapter delves into the theory and practice of background subtraction algorithms, discussing different methods such as simple subtraction, statistical techniques, frame differencing, and Gaussian Mixture Models (GMM). It provides a detailed explanation of each method, its advantages, and limitations.
• Chapter 4: Experimental Results & Analysis This chapter presents the experimental results obtained by implementing and evaluating the discussed background subtraction algorithms on real-world video sequences. It discusses performance metrics and compares the effectiveness of different methods.
• Chapter 5: Optical Flow Based Moving Object Detection This chapter introduces the concept of optical flow and explores its application in moving object detection. It focuses on the Horn-Schunck algorithm, explaining its theory and implementation for calculating optical flow.
• Chapter 6: Combined Approach for Moving Object Detection This chapter presents a combined approach that integrates background subtraction and optical flow techniques for improved moving object detection. It describes the implementation and evaluates its performance against other methods.
• Chapter 7: Shadow Detection & Removal This chapter explores the challenge of shadow detection and removal in video sequences. It discusses various techniques and presents experimental results demonstrating the effectiveness of shadow detection algorithms.

Schlüsselwörter (Keywords)

This dissertation revolves around the core concepts of moving object detection, background subtraction, video surveillance, computer vision, and image processing. It delves into various algorithms and techniques, including Gaussian Mixture Models, optical flow, and shadow detection. The research emphasizes performance evaluation, comparison, and practical implementation in real-world scenarios.