Dataset Card for VisDrone2019-DET

This is a FiftyOne version of the VisDrone2019-DET dataset with 8629 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', 'persistent`, 'overwrite' etc
dataset = fouh.load_from_hub("Voxel51/VisDrone2019-DET")

# Launch the App
session = fo.launch_app(dataset)

To render the sequential frames as video in the FiftyOne app you can use group_by() to create a view that groups the data by scene, ordered by frame number/timestamp. When you load a dynamic grouped view in the App, you'll have the same experience as video datasets:

from fiftyone import ViewField as F

view = dataset.group_by(
    "scene_id",
    order_by="frame_number"
)

# Save the view for easy loading in the App 
dataset.save_view("scenes", view)

See the gif above for details on how to render this view as a video in the app.

Dataset Details

Dataset Description

This dataset is the validation split of the VisDrone-MOT dataset.

Curated by: AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China
Language(s) (NLP): en
License: cc-by-sa-3.0
Shared by: [Harpreet Sahota](https://huggingface.co/harpreetsahota, Hacker-in-Residence at Voxel51

Dataset Sources

Repository: https://github.com/VisDrone/VisDrone-Dataset
Paper: Detection and Tracking Meet Drones Challenge

Dataset Structure

Name:        visdrone-mot
Media type:  image
Num samples: 2846
Persistent:  True
Tags:        []
Sample fields:
    id:                 fiftyone.core.fields.ObjectIdField
    filepath:           fiftyone.core.fields.StringField
    tags:               fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:           fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
    created_at:         fiftyone.core.fields.DateTimeField
    last_modified_at:   fiftyone.core.fields.DateTimeField
    scene_id:           fiftyone.core.fields.StringField
    language:           fiftyone.core.fields.StringField
    frame_number:       fiftyone.core.fields.IntField
    scene_type:         fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    time_of_day:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    pedestrian_density: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    detections:         fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)

Dataset Creation

Created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China.

Synthetically Generated Fields

This dataset has synthetically generated fields which were generated by a human annotator (Harpreet Sahota). Below is a description of the generated fields:

scene_attributes = {
    "uav0000086_00000_v": {
        "scene_type": "sporting event",
        "time_of_day": "daytime",
        "pedestrian_density": "high"
    },
    "uav0000117_02622_v": {
        "scene_type": "intersection",
        "time_of_day": "night",
        "pedestrian_density": "medium"
    },
    "uav0000137_00458_v": {
        "scene_type": "intersection",
        "time_of_day": "daytime",
        "pedestrian_density": "high"
    },
    "uav0000182_00000_v": {
        "scene_type": "road",
        "time_of_day": "daytime",
        "pedestrian_density": "low"
    },
    "uav0000268_05773_v": {
        "scene_type": "road",
        "time_of_day": "daytime",
        "pedestrian_density": "low"
    },
    "uav0000305_00000_v": {
        "scene_type": "intersection",
        "time_of_day": "daytime",
        "pedestrian_density": "low"
    },
    "uav0000339_00001_v": {
        "scene_type": "intersection",
        "time_of_day": "dusk",
        "pedestrian_density": "low"
    }
}

scene_language = {
    "uav0000086_00000_v": "A drone flies over a large crowd of people at a sporting complex where people are playing basketball.",
    "uav0000117_02622_v": "This scene shows a busy intersection at night with cars and pedestrians moving around. There seems to be a festial going on.",
    "uav0000137_00458_v": "This scene is a chaotic intersection with cars and pedestrians moving around. No one seems to be following the traffic rules.",
    "uav0000182_00000_v": "This scene shows a drone flying over a road with cars moving in both directions. The road is surrounded by trees.",
    "uav0000268_05773_v": "This scene depicts a highway with cars moving in both directions. The highway is surrounded by trees and buildings.",
    "uav0000305_00000_v": "This scene is a direct overhead shot of an intersection with cars and pedestrians moving around. Traffic seems to be orderly.",
    "uav0000339_00001_v": "This scene is a drone shot of an intersection at dusk with cars, motorcycles, and pedestrians moving around. The scene is well lit."
}

Source Data

Who are the source data producers?

The VisDrone Dataset is a large-scale benchmark created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. It contains carefully annotated ground truth data for various computer vision tasks related to drone-based image and video analysis.

Personal and Sensitive Information

The authors of the dataset have done their best to exclude identifiable information from the data to protect privacy. If you find your vehicle or personal information in this dataset, please contact them and they will remove the corresponding information from their dataset. They are not responsible for any actual or potential harm as the result of using this dataset.

Citation

BibTeX:

@ARTICLE{9573394,
  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={Detection and Tracking Meet Drones Challenge},
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3119563}}

Copyright Information

The copyright of the VisDrone dataset is reserved by the AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University, China. The dataset described on this page is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which implies that you must: (1) attribute the work as specified by the original authors; (2) may not use this work for commercial purposes ; (3) if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. The dataset is provided “as it is” and we are not responsible for any subsequence from using this dataset.

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