README.md

metadata

license: cc-by-nc-nd-4.0
task_categories:
  - image-segmentation
  - image-classification
  - object-detection
  - image-to-image
language:
  - en
tags:
  - people
  - Object Detection
  - multiple people tracking
  - human detection
dataset_info:
  features:
    - name: image_id
      dtype: int32
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: annotations
      dtype: string
  splits:
    - name: train
      num_bytes: 52028802
      num_examples: 41
  download_size: 45336774
  dataset_size: 52028802
size_categories:
  - 1K<n<10K

People Tracking & Object Detection dataset

The dataset comprises of annotated video frames from positioned in a public space camera. The tracking of each individual in the camera's view has been achieved using the rectangle tool in the Computer Vision Annotation Tool (CVAT).

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

Dataset Structure

• The images directory houses the original video frames, serving as the primary source of raw data.
• The annotations.xml file provides the detailed annotation data for the images.
• The boxes directory contains frames that visually represent the bounding box annotations, showing the locations of the tracked individuals within each frame. These images can be used to understand how the tracking has been implemented and to visualize the marked areas for each individual.

Data Format

The annotations are represented as rectangle bounding boxes that are placed around each individual. Each bounding box annotation contains the position ( xtl-ytl-xbr-ybr coordinates ) for the respective box within the frame.

💴 Buy the Dataset: This is just an example of the data. Leave a request on our website to discuss your requirements, learn about the price and buy the dataset

Leave a request on our website to discuss your requirements, learn about the price and buy the dataset.

Our Team provides high-quality data annotation tailored to your needs

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