task_categories:image-to-image task_categories:object-detection language:en license:cc-by-nc-nd-4.0 code finance 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 = {self-checkout-videos-object-tracking},
author = {TrainingDataPro},
year = {2023}
}
---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-to-image
- object-detection
tags:
- code
- finance
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': product
    - 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: 8664
    num_examples: 17
  download_size: 56150105
  dataset_size: 8664
- 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': product
    - 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: 5857
    num_examples: 10
  download_size: 35163267
  dataset_size: 5857
- 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': product
    - 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: 10586
    num_examples: 13
  download_size: 42578549
  dataset_size: 10586
---
# Products Tracking

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.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F6a6968ee80c81f187240f6ed4f8b6dfb%2Fezgif.com-gif-maker%20(1).gif?generation=1694065408131442&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=self-checkout-videos-object-tracking) to discuss your requirements, learn about the price and buy the dataset.

# Dataset structure
The dataset consists of 3 folders with video frames from self-checkouts. 
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 products tracking. For each point, the x and y coordinates are provided.  The payment status of the product is also indicated in the attribute **paid** (true, false).

# Example of the XML-file 
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F004ef6d60d61b7f94b614f5a859307fe%2Fcarbon%20(2).png?generation=1695994818122714&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=self-checkout-videos-object-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-datamarket/TrainingData_All_datasets**