Intruder Detection - Dataset Ninja

Introduction #

Tarun Bisht

The author of the Intruder Detection dataset assembled this data for a research project concerning monkey theft detection. To enhance its versatility and applicability, the dataset incorporates three additional categories beyond monkeys, comprising people, dogs, and cats.

Homepage

Summary #

Intruder Detection is a dataset for an object detection task. Possible applications of the dataset could be in the security industry.

The dataset consists of 14478 images with 28843 labeled objects belonging to 4 different classes including dog, cat, person, and other: monkey.

Images in the Intruder Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (11091 images) and test (3387 images). The dataset was released in 2021.

Here are the visualized examples for the classes:

Explore #

Intruder Detection dataset has 14478 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.

Sample annotation mask from Intruder Detection

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Have a look at 14478 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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.

Rows 1-4 of 4

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
dog➔ rectangle	4000	5509	1.38	45.17%
cat➔ rectangle	3962	4644	1.17	54.77%
person➔ rectangle	3951	15121	3.83	35.57%
monkey➔ rectangle	2565	3569	1.39	43.76%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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.

Rows 1-4 of 4

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	15121	10.05%	100%	0%	3px	0.39%	4096px	100%	240px	31.09%	2px	0.2%	4201px	100%
dog rectangle	5509	33.7%	100%	0.01%	7px	0.91%	4200px	100%	453px	57.24%	10px	0.98%	4395px	100%
cat rectangle	4644	47.65%	100%	0.27%	36px	4.69%	2401px	100%	537px	67.97%	39px	3.81%	2234px	100%
monkey rectangle	3569	32.58%	100%	0.03%	20px	1.95%	1024px	100%	468px	59.42%	11px	1.07%	1024px	100%

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.

Objects #

Table contains all 28843 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.

Rows 1-10 of 28843

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	person rectangle	a21f469218a2ddd6.jpg	768 x 1024	329px	42.84%	250px	24.41%	10.46%
2➔	cat rectangle	5bf625e3811120f9.jpg	683 x 1024	558px	81.7%	620px	60.55%	49.47%
3➔	dog rectangle	b621f2f958de73ca.jpg	768 x 1024	767px	99.87%	841px	82.13%	82.02%
4➔	monkey rectangle	2a1c261ad12e6f62.jpg	768 x 1024	250px	32.55%	313px	30.57%	9.95%
5➔	monkey rectangle	2a1c261ad12e6f62.jpg	768 x 1024	193px	25.13%	236px	23.05%	5.79%
6➔	cat rectangle	2b757019dc70148e.jpg	768 x 863	482px	62.76%	567px	65.7%	41.23%
7➔	cat rectangle	47a01ae352237ed4.jpg	685 x 1024	437px	63.8%	505px	49.32%	31.46%
8➔	person rectangle	d19448993787986b.jpg	768 x 1024	121px	15.76%	921px	89.94%	14.17%
9➔	cat rectangle	6f8fa156fa2639dd.jpg	1000 x 768	954px	95.4%	708px	92.19%	87.95%
10➔	cat rectangle	f7cc307b204766fe.jpg	1024 x 768	974px	95.12%	675px	87.89%	83.6%

License #

Intruder Detection is under DbCL v1.0 license.

Source

Citation #

If you make use of the Intruder Detection data, please cite the following reference:

@dataset{Intruder Detection,
  author={Tarun Bisht},
  title={Intruder Detection},
  year={2021},
  url={https://www.kaggle.com/datasets/tarunbisht11/intruder-detection}
}

Source

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

@misc{ visualization-tools-for-intruder-detection-dataset,
  title = { Visualization Tools for Intruder Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/intruder-detection } },
  url = { https://datasetninja.com/intruder-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Intruder Detection 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='Intruder Detection', 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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Disclaimer #

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