Intro
Preface
Contents
About the Editors
Single-Object Detection from Video Streaming
1 Introduction
2 Related Work
2.1 Two-Stage-Based Object Detection
2.2 One-Stage-Based Object Detection
3 Materials and Methods
3.1 Materials
3.1.1 Dataset
3.1.2 Data Pre-processing
3.1.3 Data Augmentation
3.1.4 Data Annotation
3.2 Methods
3.2.1 Overview of YOLOv4
4 Applications of YOLOv4
5 Proposed Methodology
6 Experimentation and Implementation
6.1 Experimental Setup
6.2 Training YOLOv4
6.3 Evaluation Measures

7 Results and Performance Analysis
8 Conclusion and Future Work
References
Different Approaches to Background Subtraction and Object Tracking in Video Streams: A Review
1 Introduction
2 Literature Review
2.1 Survey on Frame Rate Conversion Techniques
2.2 Survey on Foreground Extraction Techniques
2.3 Feature Extraction Methods
2.4 Machine Learning Approaches for Pedestrian Detection
2.5 Deep Learning Approaches for Pedestrian Detection
3 Conclusion and Future Scope
References

Auto Alignment of Tanker Loading Arm Utilizing Stereo Vision Video and 3D Euclidean Scene Reconstruction
1 Introduction
2 State of Research in the Field
2.1 Stereo View Geometry
2.2 Geometry of an Epipolar Camera
2.3 Marine Loading Arms
3 Research Methodology
3.1 Disparity Map
3.2 Calibration
3.3 Feature Recognition
3.4 Calibration and Model Fitting
3.5 Distance Calculation
3.6 Reconstruction Error
4 Experimentation and Results
4.1 Extraction of a Specific Target
4.2 Results of Calibration
4.3 Reconstruction Error

4.4 Effects of Errors on 3D Reconstruction
5 Conclusion and Future Scope
References
Visual Object Segmentation Improvement Using Deep Convolutional Neural Networks
1 Introduction
2 Methods for Image Retrieval
2.1 Image Retrieval Techniques
3 Literature Review
4 Data Analysis of Visual Object Segmentation
5 Pre-processing for Anatomical MRI Source Estimation
5.1 Pre-processing for Anatomical Parcellation Labels
6 Source Activity Reconstruction and Encoding Model
7 Nested Cross-Validation and Encoding Linear Model

8 Encoding and Decoding of Pixel Space Control Model
8.1 Improved Cascade Mask R-CNN Model
9 Conclusion and Future Scope
References
Applications of Deep Learning-Based Methods on Surveillance Video Stream by Tracking Various Suspicious Activities
1 Introduction
2 Methods for Detecting Video Anomalies Based on Deep Learning
2.1 Using Patterns as a Global Framework
2.2 Methods Based on Grid Patterns
2.3 Learning Models Based on Representations
2.4 Discriminative Models
2.5 Models of Deep One-Class Categorization
2.6 Models with Deep Hybridization