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Scut Head Dataset

44051337
Tagsurveillance
Taskobject detection
Release YearMade in 2018
Licensecustom
Download551 MB

Introduction #

Released 2018-11-24 ·Dezhi Peng, Zikai Sun, Zirong Chenet al.

The authors have collected and labeled a new large Scut Head Dataset which includes 4405 images with 111251 heads annotated. The dataset consists of two parts. part a includes 2000 images sampled from monitor videos of classrooms in an university with 67321 heads annotated. part b includes 2405 images crawled from internet with 43930 heads annotated. They have labelled every visable heads with xmin, ymin, xmax and ymax coordinates and ensured that annotations cover the entire head including the blocked parts but without extra background. Both part a and part b are divided into training and testing parts.

Motivation

Face detection and pedestrian detection represent pivotal challenges in the field of computer vision, and notable advancements have been made in recent years. However, practical applications reveal certain limitations. For instance, face detection algorithms are restricted to identifying frontal faces, rendering individuals facing away from the camera undetectable. Similarly, in complex indoor environments, where body parts may be obscured, pedestrian detection encounters difficulties. Head detection, on the other hand, presents fewer limitations and proves more adept at locating and enumerating individuals, particularly in indoor settings. Nevertheless, detecting heads indoors poses its own set of challenges, including variations in scale and appearance, as well as the task of identifying smaller heads. Addressing these challenges necessitates effective exploitation of extracted features to accurately localize heads and discern them from the background—a formidable task. Prior approaches have often relied on leveraging multi-scale features derived from different layers of deep convolutional neural networks to tackle these complexities.

Detecting small heads presents a distinct challenge in head detection tasks. To address this, the authors have introduced a cascaded multi-scale architecture tailored specifically for small head detection. Their approach centers on refining local detection outcomes by enhancing the resolution of image segments. The proposed architecture comprises two distinct detectors: a global detector and a local detector. The global detector is tasked with identifying large heads and subsequently relays this information to the local detector, which then focuses on analyzing enlarged segments containing small heads for more precise detection. Additionally, owing to the scarcity of datasets dedicated to head detection, the authors have curated and annotated a large-scale dataset specifically for this purpose, named SCUT-HEAD.

Dataset description

The authors have curated and annotated a comprehensive head detection dataset named SCUT-HEAD, which comprises two distinct parts. part a consists of 2000 images extracted from surveillance videos captured in university classrooms, encompassing a total of 67,321 annotated heads. To ensure dataset diversity and reduce similarity, representative images were thoughtfully selected, considering the inherent similarities across classroom settings.

image

An example image and annotations of Part A in SCUT-HEAD.

On the other hand, part b consists of 2405 images sourced from the internet, with 43,930 annotated heads. Each visible head in both part a and part b is meticulously labeled with coordinates (xmin, ymin, xmax, ymax), encompassing the entire head region while excluding any extraneous background.

image

An example image and annotations of Part B in SCUT-HEAD.

Both part a and part b are further divided into training and testing subsets. Specifically, part a includes 1500 images for training and 500 for testing, while part b comprises 1905 training images and 500 testing images.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Scut Head Dataset is a dataset for an object detection task. It is used in the surveillance industry.

The dataset consists of 4405 images with 111248 labeled objects belonging to 1 single class (person).

Images in the Scut Head dataset have bounding box annotations. There is 1 unlabeled image (i.e. without annotations). There are 3 splits in the dataset: train (2543 images), test (1000 images), and val (862 images). Additionally, every image marked with part a or part b tag. The dataset was released in 2018 by the South University of Technology, China.

Dataset Poster

Explore #

Scut Head dataset has 4405 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
OpenSample annotation mask from Scut HeadSample image from Scut Head
👀
Have a look at 4405 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-1 of 1
Class
Images
Objects
Count on image
average
Area on image
average
person
rectangle
4404
111248
25.26
8.46%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-1 of 1
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
person
rectangle
111248
0.35%
23.82%
0.01%
3px
0.55%
1357px
74.77%
40px
6.24%
4px
0.44%
1269px
38.93%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 99528 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 99528
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
person
rectangle
PartB_00514.jpg
267 x 400
54px
20.22%
47px
11.75%
2.38%
2
person
rectangle
PartB_00514.jpg
267 x 400
33px
12.36%
25px
6.25%
0.77%
3
person
rectangle
PartB_00514.jpg
267 x 400
42px
15.73%
35px
8.75%
1.38%
4
person
rectangle
PartB_00514.jpg
267 x 400
34px
12.73%
24px
6%
0.76%
5
person
rectangle
PartB_00514.jpg
267 x 400
28px
10.49%
22px
5.5%
0.58%
6
person
rectangle
PartB_00514.jpg
267 x 400
29px
10.86%
21px
5.25%
0.57%
7
person
rectangle
PartB_00514.jpg
267 x 400
45px
16.85%
52px
13%
2.19%
8
person
rectangle
PartB_00514.jpg
267 x 400
55px
20.6%
56px
14%
2.88%
9
person
rectangle
PartA_01076.jpg
715 x 1259
53px
7.41%
45px
3.57%
0.26%
10
person
rectangle
PartA_01076.jpg
715 x 1259
22px
3.08%
23px
1.83%
0.06%

License #

The SCUT-HEAD database is free to the academic community for research purpose usage only.

Source

Citation #

If you make use of the Scut Head data, please cite the following reference:

@article{peng2018detecting,
  title={Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture},
  author={Peng, Dezhi and Sun, Zikai and Chen, Zirong and Cai, Zirui and Xie, Lele and Jin, Lianwen},
  journal={arXiv preprint arXiv:1803.09256},
  year={2018}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-scut-head-dataset,
  title = { Visualization Tools for Scut Head Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/scut-head } },
  url = { https://datasetninja.com/scut-head },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset Scut Head can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Scut Head', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

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