Automated Author ProfileKhalzaa, Khulan
Khalzaa, Khulan
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 6.1 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset provides a comprehensive collection of traffic scene datasets, categorized into four main groups: Traffic Scene Datasets, Top-View Datasets, MultiHeightView Datasets and Depth Datasets. Dataset includes both real-world and synthetic datasets, designed to support research in robotics, self driving car with computer vision. These datasets are intended to assist in various research areas, such as road user detection, top-view traffic analysis, traffic pattern recognition, depth information extraction, and infrastructure monitoring using aerial views in urban environments. The dataset approach encourages synthetic data utilization in regions with privacy restrictions, where public video recording is legally and culturally constrained. It demonstrates how limited real-world data can be augmented with photorealistic synthetic scenes for research.and vision model training and testing.
Authors
- Khalzaa, Khulan
This dataset provides a comprehensive collection of traffic scene datasets, categorized into four main groups: Traffic Scene Datasets, Top-View Datasets, MultiHeightView Datasets and Depth Datasets. Dataset includes both real-world and synthetic datasets, designed to support research in robotics, self driving car with computer vision. These datasets are intended to assist in various research areas, such as road user detection, top-view traffic analysis, traffic pattern recognition, depth information extraction, and infrastructure monitoring using aerial views in urban environments. The dataset approach encourages synthetic data utilization in regions with privacy restrictions, where public video recording is legally and culturally constrained. It demonstrates how limited real-world data can be augmented with photorealistic synthetic scenes for research.and vision model training and testing.
Authors
- Khalzaa, Khulan
This dataset provides a comprehensive collection of traffic scene datasets, categorized into three main groups: Traffic Scene Datasets, Top-View Datasets, and Depth Datasets. Each group includes both real-world and synthetic datasets, designed to support research in autonomous driving and computer vision. These datasets are intended to assist in various research areas, such as road user detection, top-view traffic analysis, traffic pattern recognition, depth information extraction, and infrastructure monitoring using aerial views in urban environments.
Authors
- Khalzaa, Khulan
This dataset provides a comprehensive collection of traffic scene datasets, categorized into three main groups: Traffic Scene Datasets, Top-View Datasets, and Depth Datasets. Each group includes both real-world and synthetic datasets, designed to support research in autonomous driving and computer vision. These datasets are intended to assist in various research areas, such as road user detection, top-view traffic analysis, traffic pattern recognition, depth information extraction, and infrastructure monitoring using aerial views in urban environments.
Authors
- Khalzaa, Khulan