A Database Model for Querying Visual Surveillance Videos by Integrating Semantic and Low Level Features 1st Edition by Ediz Saykol, Ugur Gudukbay, Ozgur Ulusoy ISBN 9783540319450

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ISBN 10: 
ISBN 13: 9783540319450
Author: Ediz Saykol, Ugur Gudukbay, Ozgur Ulusoy 

Automated visual surveillance has emerged as a trendy application domain in recent years. Many approaches have been developed on video processing and understanding. Content-based access to surveillance video has become a challenging research area. The results of a considerable amount of work dealing with automated access to visual surveillance have appeared in the literature. However, the event models and the content-based querying and retrieval components have significant gaps remaining unfilled. To narrow these gaps, we propose a database model for querying surveillance videos by integrating semantic and low-level features. In this paper, the initial design of the database model, the query types, and the specifications of its query language are presented.

A Database Model for Querying Visual Surveillance Videos by Integrating Semantic and Low-Level Features 1st Table of contents:

1. Background and Related Work

   • 2.1 Video Surveillance: Techniques and Technologies
   • 2.2 Traditional Approaches to Video Querying
   • 2.3 Semantic Features in Video Analysis
   • 2.4 Low-Level Features and Their Importance in Video Retrieval
   • 2.5 Existing Database Models for Video Retrieval
   • 2.6 Integration of Semantic and Low-Level Features in Video Querying

2. Understanding Low-Level Features in Visual Surveillance

   • 3.1 Definition and Examples of Low-Level Features
   • 3.2 Techniques for Extracting Low-Level Features (e.g., Color, Texture, Motion)
   • 3.3 Applications of Low-Level Features in Video Surveillance
   • 3.4 Limitations of Low-Level Features in Complex Video Analysis
   • 3.5 Enhancing Low-Level Features with Higher-Level Semantics

3. Semantic Features in Video Analysis

   • 4.1 Defining Semantic Features in Visual Surveillance
   • 4.2 Object Recognition and Classification
   • 4.3 Activity and Event Detection
   • 4.4 Scene Understanding and Contextual Information
   • 4.5 The Role of Machine Learning in Semantic Feature Extraction
   • 4.6 Challenges in Extracting and Representing Semantic Information

4. Database Models for Video Retrieval

   • 5.1 Introduction to Video Databases and Querying Techniques
   • 5.2 Relational vs. Non-Relational Database Models for Video Data
   • 5.3 Indexing Methods for Video Retrieval
   • 5.4 Querying Methods: Keyword-based, Content-based, and Hybrid Approaches
   • 5.5 Challenges in Video Data Storage, Access, and Management

5. Proposed Database Model for Video Querying

   • 6.1 Overview of the Proposed Model
   • 6.2 Data Representation: Semantic and Low-Level Features
   • 6.3 Integrating Video Features into a Unified Database Schema
   • 6.4 Indexing Techniques for Efficient Querying
   • 6.5 Query Language and Query Execution Model
   • 6.6 Performance Optimization Strategies in the Database Model

6. Video Querying by Integrating Semantic and Low-Level Features

   • 7.1 Query Types: Text-Based, Feature-Based, and Hybrid Queries
   • 7.2 Semantic Querying: Leveraging Contextual and High-Level Information
   • 7.3 Low-Level Feature Querying: Visual Similarity and Motion Patterns
   • 7.4 Hybrid Querying: Combining Low-Level and Semantic Features for Effective Retrieval
   • 7.5 Query Processing and Ranking Methods

7. Implementation of the Proposed Model

   • 8.1 System Architecture and Components
   • 8.2 Video Data Collection and Preprocessing
   • 8.3 Extraction of Low-Level and Semantic Features
   • 8.4 Integrating Features into the Database Model
   • 8.5 Query Interface and User Interaction
   • 8.6 Software Tools and Platforms Used

8. Experimental Evaluation and Case Studies

   • 9.1 Evaluation Metrics for Video Querying Systems
   • 9.2 Benchmark Datasets for Surveillance Videos
   • 9.3 Performance Evaluation: Precision, Recall, and Query Efficiency
   • 9.4 Comparative Analysis with Existing Video Querying Systems
   • 9.5 Case Study 1: Querying for Specific Activities in Surveillance Videos
   • 9.6 Case Study 2: Detecting and Retrieving Objects in Complex Scenes
   • 9.7 Results and Discussion

9. Challenges and Limitations

• 10.1 Handling Large-Scale Video Datasets
• 10.2 Real-Time Querying and Video Processing Constraints
• 10.3 Dealing with Noisy and Incomplete Data
• 10.4 Improving the Robustness of Semantic and Low-Level Feature Integration
• 10.5 Limitations of the Current Approach and Areas for Improvement

11. Future Directions and Research Opportunities

• 11.1 Extending the Database Model to Multi-Modal Video Data
• 11.2 Real-Time Video Querying and Processing Techniques
• 11.3 Integration with Advanced Machine Learning Models (Deep Learning, Reinforcement Learning)
• 11.4 Enhancing Querying Capabilities with Natural Language Processing (NLP)
• 11.5 Cross-Domain Applications of Video Querying Models

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