init dataset card

README.md

@@ -1,3 +1,61 @@
----
-license: cc-by-4.0
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+---
+license: cc-by-4.0
+tags:
+- LiDAR,
+- Point cloud
+- Point cloud augmentation
+- Object detection
+- People detection
+size_categories:
+- 100K<n<1M
+---
+# Dataset Card for PointCloudPeople
+
+The use of light detection and ranging (LiDAR) sensor technology for people detection offers a significant advantage in terms of data protection. However, to design 
+these systems cost- and energy-efficiently, the relationship between the measurement data and final object detection output with deep neural networks (DNNs) has to be 
+elaborated. Therefore, we present augmentation methods to analyze the influence of the LiDAR sensor parameters distance, resolution, noise, and shading 
+in real point clouds for people detection.
+
+## Dataset Details
+
+The data set is structured as follows. As described in the corresponding paper, an original data set consisting of point clouds in .pcd format was recorded with a 
+Blickfeld Qb2 LiDAR sensor and automatically labeled in .json format using GDPR-compliant cameras (Basic). This dataset was then augmented with different sensor and 
+mounting parameters (distance, noise, resolution, shading).
+
+- **Curated by:** Lukas Haas
+- **License:** CC-BY-4.0
+
+### Dataset Sources
+
+The data set consists of approx—2000 point clouds with the corresponding labels per sub-data set collected with a Blickfeld Qb2 LiDAR Sensor. The augmentation generated a
+total of 95 derivatives of the dataset. The data set contains at least one person per point cloud and other interfering objects in the sensor's FOV. The subjects in the 
+FoV are of different sizes.
+
+### Dataset Structure
+
+Based on the measurement setup presented in the paper a dataset is recorded with the Qb2 to train the DNNs. Based on this dataset, different datasets with various 
+resolutions, distances to the scene, noise, and shading of people in the point cloud of the LiDAR sensor are augmented to investigate the individual influence of the 
+parameters on the object detection performance of DNNs. The point clouds are in .pcd files with the x, y, z, intensit format, and the
+labels in .json files in the unified 3D box definition label format for each object.
+
+### Personal and Sensitive Information
+
+Due to the anonymity of people in LiDAR point clouds, personal data is protected. Furthermore, GDPR conform cameras are used to label the point clouds automatically.
+
+## Bias, Risks, and Limitations
+
+Considering the limitations of our dataset, real-world tests should be conducted. GDPR conforms with care in a safe environment.
+However, it should be noted that although the data set includes people of different body sizes, the majority of people are adults due to the location of the measurement 
+setup.
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]