README.md

---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-to-image
- object-detection
tags:
- electric scooter
- e-scooter
- vehicle tracking
- object detection
- multiple-object vehicle tracking
dataset_info:
- config_name: video_01
  features:
  - name: id
    dtype: int32
  - name: name
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: shapes
    sequence:
    - name: track_id
      dtype: uint32
    - name: label
      dtype:
        class_label:
          names:
            '0': electric_scooter
    - name: type
      dtype: string
    - name: points
      sequence:
        sequence: float32
    - name: rotation
      dtype: float32
    - name: occluded
      dtype: uint8
    - name: attributes
      sequence:
      - name: name
        dtype: string
      - name: text
        dtype: string
  splits:
  - name: train
    num_bytes: 9312
    num_examples: 22
  download_size: 8409013
  dataset_size: 9312
- config_name: video_02
  features:
  - name: id
    dtype: int32
  - name: name
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: shapes
    sequence:
    - name: track_id
      dtype: uint32
    - name: label
      dtype:
        class_label:
          names:
            '0': electric_scooter
    - name: type
      dtype: string
    - name: points
      sequence:
        sequence: float32
    - name: rotation
      dtype: float32
    - name: occluded
      dtype: uint8
    - name: attributes
      sequence:
      - name: name
        dtype: string
      - name: text
        dtype: string
  splits:
  - name: train
    num_bytes: 10583
    num_examples: 25
  download_size: 48396353
  dataset_size: 10583
- config_name: video_03
  features:
  - name: id
    dtype: int32
  - name: name
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: shapes
    sequence:
    - name: track_id
      dtype: uint32
    - name: label
      dtype:
        class_label:
          names:
            '0': electric_scooter
    - name: type
      dtype: string
    - name: points
      sequence:
        sequence: float32
    - name: rotation
      dtype: float32
    - name: occluded
      dtype: uint8
    - name: attributes
      sequence:
      - name: name
        dtype: string
      - name: text
        dtype: string
  splits:
  - name: train
    num_bytes: 8466
    num_examples: 20
  download_size: 13600750
  dataset_size: 8466
---
# Electric Scooters Tracking - Object Detection dataset

The dataset contains frames extracted from videos with people riding electric scooters. Each frame is accompanied by **bounding box** that specifically **tracks the electric scooter** in the image. 

# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[our website](https://unidata.pro/datasets/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=electric-scooters-tracking)** to buy the dataset

This dataset can be useful for *object detection, motion tracking, behavior analysis, autonomous vehicle development and smart city*. 

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F413e8303b798767f9c30450e0ad8b19b%2Fezgif.com-gif-maker.gif?generation=1695151025014061&alt=media)

# Dataset structure
The dataset consists of 3 folders with frames from the video with people riding an electric scooter. 
Each folder includes:
- **images**: folder with original frames from the video,
- **boxes**: visualized data labeling for the images in the previous folder,
- **.csv file**: file with id and path of each frame in the "images" folder,
- **annotations.xml**: contains coordinates of the bounding boxes and labels, created for the original frames

# Data Format

Each frame from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes for electric scooter tracking. For each point, the x and y coordinates are provided.

# Example of the XML-file 
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Ff7bf13348e01369a8ccab9d5bf2acac6%2Fcarbon.png?generation=1695994913297718&alt=media)

# Object tracking might be made in accordance with your requirements.

# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[our website](https://unidata.pro/datasets/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=electric-scooters-tracking)** to discuss your requirements, learn about the price and buy the dataset
Leave a request on [our website](https://unidata.pro/datasets/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=electric-scooters-tracking) to discuss your requirements, learn about the price and buy the dataset.

## [Our Team](https://unidata.pro/datasets/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=electric-scooters-tracking) provides high-quality data annotation tailored to your needs

*keywords: electric scooter gps, e-scooter, e-bike, navigation, vehicle tracking algorithm, vehicle tracking dataset, object detection, multiple-object vehicle tracking, vehicle image dataset, labeled web tracking dataset, image dataset, classification, computer vision, machine learning, cctv, camera detection, surveillance, security camera, security camera object detection, video-based monitoring, smart city, smart city development, smart city vision, smart city deep learning, smart city management*