Update README.md

README.md

@@ -27,7 +27,6 @@ pretty_name: PP4AV
 - [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
   - [Languages](#languages)
 - [Dataset Structure](#dataset-structure)
   - [Data Instances](#data-instances)
@@ -58,10 +57,6 @@ pretty_name: PP4AV
 
 PP4AV is the first public dataset with faces and license plates annotated with driving scenarios. P4AV provides 3,447 annotated driving images for both faces and license plates. For normal camera data, dataset sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. This dataset use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. PP4AV dataset can be used as a benchmark suite (evaluating dataset) for data anonymization models in autonomous driving.
 
-### Supported Tasks and Leaderboards
-
-- `face-detection`: The dataset can be used to train a model for Face Detection. More information on evaluating the model's performance can be found [here](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html).
-
 ### Languages
 
 English
@@ -74,7 +69,7 @@ A data point comprises an image and its face and license plate annotations.
 
 ```
 {
-  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'objects': {
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x19FA12186D8>, 'objects': {
     'bbox': [
       [0 0.230078 0.317081 0.239062 0.331367],
       [1 0.5017185 0.0306425 0.5185935 0.0410975],
@@ -109,21 +104,29 @@ A data point comprises an image and its face and license plate annotations.
 
 ## Dataset Creation
 
-### Curation Rationale
-
-The curators state that the current face detection datasets typically contain a few thousand faces, with limited variations in pose, scale, facial expression, occlusion, and background clutters,
-making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping
-with heavy occlusion, small scale, and atypical pose.
-
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-The objective of PP4AV is to build a benchmark dataset that can be used to evaluate face and license plate detection models for autonomous driving. For normal camera data, we sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. We focus on sampling data in urban areas rather than highways in order to provide sufficient samples of license plates and pedestrians. The images in PP4AV were sampled from **6** European cities at various times of day, including nighttime. We use the fisheye images from the WoodScape dataset to select **244** images from the front, rear, left, and right cameras for fisheye camera data. In total, **3,447** images were selected and annotated in PP4AV.
+The objective of PP4AV is to build a benchmark dataset that can be used to evaluate face and license plate detection models for autonomous driving. For normal camera data, we sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. We focus on sampling data in urban areas rather than highways in order to provide sufficient samples of license plates and pedestrians. The images in PP4AV were sampled from **6** European cities at various times of day, including nighttime. The source data from 6 cities in European was described as follow:
+  - `Paris`: [paris_youtube_video](https://www.youtube.com/watch?v=nqWtGWymV6c)
+  - `Netherland day time`: [netherland_youtube_video]()
+  - `Netherland night time`: Hague, Amsterdam [netherland_youtube_video]()
+  - `Switzerland`: [switzerland_youtube_video](https://www.youtube.com/watch?v=0iw5IP94m0Q)
+  - `Zurich`: [zurich images data](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
+  - `Stutgatt`: [stutgatt images data](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
+  - `Strasbourg`: [strasbourg images data](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
+
+We use the fisheye images from the WoodScape dataset to select **244** images from the front, rear, left, and right cameras for fisheye camera data. 
+The source of fisheye data for sampling is located at WoodScape's [fisheye images](https://woodscape.valeo.com/download).
+
+In total, **3,447** images were selected and annotated in PP4AV.
+
+#### Source data?
 
-#### Who are the source language producers?
+The images are selected from publicly available dataset.
+Here is the public available dataset using for conducting this dataset:
 
-The images are selected from publicly available WIDER dataset.
 
 ### Annotations