Pedestrian Detection - Dataset Ninja

Introduction #

Karthika N. J., Chandran Saravanan

Pedestrian Detection dataset addresses the challenge of false positives in person detection, a critical subfield of object detection essential for applications like person tracking, intelligent surveillance systems, and autonomous vehicles. consists of 944 train images, 160 val images, and 235 test images, with a total of 1626 person and 1368 person-like labelling.

Some objects have very similar features to those of a person. If a model is trained using a dataset containing only persons, it leads to several false positives since it cannot differentiate such objects from that of a person. Our dataset includes person and person-like objects (PnPLO). Person-like objects that the authors introduce in our dataset are statues, mannequins, scarecrows, and robots. Authors used the SSD model to show that, on training a model using our dataset, authors can significantly reduce the false positives during detection when compared to models trained on standard person datasets, thereby improving the precision.

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Research Paper

Summary #

Pedestrian Detection is a dataset for an object detection task. It is used in the surveillance industry.

The dataset consists of 1339 images with 3166 labeled objects belonging to 5 different classes including person, person-like, head, and other: hand and foot.

Images in the Pedestrian Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (944 images), test (235 images), and val (160 images). Also, objects in the dataset have pose tag. Explore them in the supervisely labeling tool. The dataset was released in 2020 by the National Institute of Technology, India.

Explore #

Pedestrian Detection dataset has 1339 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 Pedestrian Detection

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Have a look at 1339 images

Because of dataset's license preview is limited to 12 images

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

Class balance #

There are 5 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 1-5 of 5

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
person➔ rectangle	820	1626	1.98	40.66%
person-like➔ rectangle	601	1368	2.28	46.81%
head➔ rectangle	32	47	1.47	4.36%
hand➔ rectangle	32	79	2.47	1.83%
foot➔ rectangle	18	46	2.56	0.89%

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.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

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 1-5 of 5

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	1626	22.51%	99.53%	0.05%	20px	3.41%	1312px	99.8%	231px	56.59%	12px	1.33%	984px	99.8%
person-like rectangle	1368	21.54%	99.01%	0.09%	31px	4.95%	3455px	99.88%	497px	67.44%	19px	1.79%	2369px	99.83%
hand rectangle	79	0.77%	8.64%	0.04%	8px	2.19%	150px	40%	30px	7.62%	8px	1.6%	120px	24%
head rectangle	47	2.97%	31.86%	0.1%	15px	4.1%	251px	66.93%	64px	16.1%	11px	2.2%	238px	47.6%
foot rectangle	46	0.37%	1.89%	0.03%	5px	1.37%	56px	13.15%	22px	5.65%	9px	1.8%	81px	24.25%

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 3166 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 3166

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-like rectangle	image (787).jpg	900 x 900	793px	88.11%	279px	31%	27.31%
2➔	person-like rectangle	image (757).jpg	720 x 1280	683px	94.86%	1114px	87.03%	82.56%
3➔	person rectangle	image (304).jpg	500 x 334	420px	84%	230px	68.86%	57.84%
4➔	person rectangle	image (133).jpg	375 x 500	322px	85.87%	252px	50.4%	43.28%
5➔	person-like rectangle	image (570).jpg	400 x 600	361px	90.25%	102px	17%	15.34%
6➔	person-like rectangle	image (570).jpg	400 x 600	252px	63%	85px	14.17%	8.93%
7➔	person-like rectangle	image (570).jpg	400 x 600	331px	82.75%	97px	16.17%	13.38%
8➔	person-like rectangle	image (570).jpg	400 x 600	391px	97.75%	111px	18.5%	18.08%
9➔	person-like rectangle	image (570).jpg	400 x 600	361px	90.25%	126px	21%	18.95%
10➔	person rectangle	image (570).jpg	400 x 600	329px	82.25%	121px	20.17%	16.59%

License #

License is unknown for the Pedestrian Detection dataset.

Source

Citation #

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

@InProceedings{10.1007/978-981-15-7031-5_103,
  author="Karthika, N. J. and Chandran, Saravanan",
  editor="Mallick, Pradeep Kumar and Meher, Preetisudha and Majumder, Alak and Das, Santos Kumar",
  title="Addressing the False Positives in Pedestrian Detection",
  booktitle="Electronic Systems and Intelligent Computing",
  year="2020",
  publisher="Springer Singapore",
  address="Singapore",
  pages="1083--1092",
  isbn="978-981-15-7031-5"
}

Source

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

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

Download #

Please visit dataset homepage to download the data.

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