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Intruder Detection Dataset

1447842056
Tagsecurity, surveillance
Taskobject detection
Release YearMade in 2021
LicenseDbCL v1.0
Download4 GB

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.

Dataset LinkHomepage

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.

OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image from Intruder Detection
OpenSample annotation mask from Intruder DetectionSample image 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.

Search
Rows 1-4 of 4
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
dogβž”
rectangle
4000
5509
1.38
45.17%
catβž”
rectangle
3962
4644
1.17
54.77%
personβž”
rectangle
3951
15121
3.83
35.57%
monkeyβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 28843
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
person
rectangle
a21f469218a2ddd6.jpg
768 x 1024
329px
42.84%
250px
24.41%
10.46%
2βž”
cat
rectangle
5bf625e3811120f9.jpg
683 x 1024
558px
81.7%
620px
60.55%
49.47%
3βž”
dog
rectangle
b621f2f958de73ca.jpg
768 x 1024
767px
99.87%
841px
82.13%
82.02%
4βž”
monkey
rectangle
2a1c261ad12e6f62.jpg
768 x 1024
250px
32.55%
313px
30.57%
9.95%
5βž”
monkey
rectangle
2a1c261ad12e6f62.jpg
768 x 1024
193px
25.13%
236px
23.05%
5.79%
6βž”
cat
rectangle
2b757019dc70148e.jpg
768 x 863
482px
62.76%
567px
65.7%
41.23%
7βž”
cat
rectangle
47a01ae352237ed4.jpg
685 x 1024
437px
63.8%
505px
49.32%
31.46%
8βž”
person
rectangle
d19448993787986b.jpg
768 x 1024
121px
15.76%
921px
89.94%
14.17%
9βž”
cat
rectangle
6f8fa156fa2639dd.jpg
1000 x 768
954px
95.4%
708px
92.19%
87.95%
10βž”
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 = { jul },
  note = { visited on 2024-07-25 },
}

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.

. . .

Disclaimer #

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