task_categories:image-to-image task_categories:object-detection language:en license:cc-by-nc-nd-4.0 code legal region:us
The dataset contains frames extracted from self-checkout videos, specifically focusing
on **tracking products**. The tracking data provides the **trajectory of each product**,
allowing for analysis of customer movement and behavior throughout the transaction.
The dataset assists in detecting shoplifting and fraud, enhancing efficiency, accuracy,
and customer experience. It facilitates the development of computer vision models for
*object detection, tracking, and recognition* within a self-checkout environment.
@InProceedings{huggingface:dataset,
title = {electric-scooters-tracking},
author = {TrainingDataPro},
year = {2023}
}
---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-to-image
- object-detection
tags:
- code
- legal
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

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. 

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)

# Get the dataset

### This is just an example of the data

Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=electric-scooters-tracking) to discuss your requirements, learn about the price and buy the dataset.

# 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.

## [TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=electric-scooters-tracking) provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**

TrainingData's GitHub: **https://github.com/trainingdata-pro**