Preface
Acknowledgements
Contents
1 Art Graffiti Detection in Urban Images Using Deep Learning
1.1 Introduction
1.2 Detection of Art Graffiti
1.3 Object Detection Deep Architectures
1.3.1 YOLO Models
1.3.2 YOLOv4
1.3.3 YOLOv5
1.3.4 YOLOv4-tiny
1.4 Dataset
1.5 Experiments
1.5.1 Evaluation Metrics and Computing Resources
1.5.2 Experiments
1.6 Conclusion
References
2 Deep Neural Networks for Passengers' Density Estimation and Face Mask Detection for COVID-19 in Public Transportation Services
2.1 Introduction
2.2 Proposed Architecture
2.3 Deep Learning for Object Detection
2.4 Experiments and Results
2.5 Conclusions and Future Work
References
3 Epistemic Uncertainty Quantification in Human Trajectory Prediction
3.1 Introduction
3.2 Related Work and Problem Statement
3.3 Quantification of Aleatoric and Epistemic Uncertainties
3.3.1 Bayesian Deep Learning
3.3.2 Uncertainty Estimation Through Bayesian Deep Learning
3.4 Evaluating and Calibrating Uncertainties
3.4.1 Calibration of Uncertainties in 1D Regression
3.4.2 Highest Density Regions
3.4.3 HDR-Based Calibration
3.5 Experiments
3.5.1 Trajectory Prediction Base Model and Bayesian Variants
3.5.2 Implementation Details
3.5.3 Evaluation of the Uncertainties Quality
3.5.4 Evaluation of the Re-calibration Process
3.6 Conclusions
References
4 Automatic Detection of Knives in Complex Scenes
4.1 Introduction
4.2 Related Work
4.3 YOLOv4 Architecture for Detection of Knives
4.3.1 Detection of Knives
4.3.2 YOLOv4
4.4 Datasets
4.4.1 DaSCI Dataset
4.4.2 MS COCO Dataset
4.4.3 Knife Classification Datasets
4.5 Pre-processings on Dataset
4.5.1 Dataset Preparation
4.5.2 Dataset Variabilities
4.5.3 Transfer Learning
4.6 Experimental Results
4.6.1 Description of Performance Metrics
4.6.2 Experimental Results
4.6.3 General Results
4.6.4 Results Considering Variabilities in Images
4.7 Conclusion
References
5 Human Body Pose Estimation in Multi-view Environments
5.1 Introduction
5.2 Camera Pose Estimation
5.2.1 Siamese Network Architecture
5.2.2 Results from Real World Datasets
5.2.3 From Virtual Environments to Real World
5.3 Human Pose Estimation
5.3.1 Multi-view Scheme
5.3.2 Results from Multi-view Approach
5.4 Conclusions
References
6 Video Analytics in Urban Environments: Challenges and Approaches
6.1 Introduction
6.2 Image Preprocessing
6.2.1 Camera Calibration
6.2.2 Background Subtraction
6.2.3 Image and Content Enhancement
6.3 Detection
6.4 Classification
6.5 Tracking
6.6 Applications
6.6.1 Traffic Scenarios
6.6.2 Pedestrian-Oriented Applications
6.7 Datasets
6.8 Conclusions
References
7 Multimodal Sensor Calibration Approaches in the ATLASCAR Project
7.1 Introduction
7.2 Calibration of 3D and 2D LiDARs with Conical Targets
7.2.1 Proposed Calibration Process
7.2.2 Results
7.3 Spherical Target to Calibrate LiDARs and Cameras
7.3.1 Principles of the Calibration Approach
7.3.2 Geometric Transformations from Sets of Ball Centers
7.3.3 Results
7.4 Deep Learning to Detect a Sphere in Multimodal Images
7.4.1 Detection of the Calibration Target using the Same Neural Network
7.4.2 Conversion of the Coordinates of the Center of the Ball to Meters
7.4.3 Transformation Matrix Calculation
7.4.4 Results
7.5 Optimization Approach for Calibration
7.5.1 The Methodology
7.5.2 Results
7.6 Comparative Analysis of the Techniques
7.7 Conclusion and Future Perspectives
References
8 Early Computer-Aided Diagnose in Medical Environments: A Deep Learning Based Lightweight Solution
8.1 Introduction
8.2 Methodology
8.2.1 Preprocessing
8.2.2 Attention Residual Learning
8.2.3 EfficientNets
8.3 Experiments and Results
8.3.1 Impact of the Preprocessing Methods in the Classification
8.3.2 Attention Residual Learning on EfficientNet
8.4 Conclusions
References
9 Melamine Faced Panel Inspection, Towards an Efficient Use of Natural Resources
9.1 Introduction
9.2 Related Works
9.3 Theoretical Background
9.3.1 Local Binary Pattern (LBP)
9.3.2 Support Vector Machine (SVM)
9.4 Used Methodology for Surface Classification
9.4.1 Used Dataset
9.4.2 Dealing with Imbalanced Data
9.4.3 Used Programming Tools
9.4.4 Features Computation
9.4.5 Classification Training and Testing
9.5 Results
9.6 Comparisons with Previous Work
9.7 Conclusion
References
10 Waste Classification with Small Datasets and Limited Resources
10.1 Introduction
10.2 Related Work
10.2.1 Waste Recycling
10.2.2 Network Distillation
10.3 Reviewing Network Distillation
10.4 Proposed Validation Protocol and Experimental Results
10.4.1 The TrashNet Dataset
10.4.2 Data Preprocessing
10.4.3 Evaluation
10.4.4 Implementation Details
10.4.5 Results
10.5 Conclusions and Future Work
References