Dataset Ninja LogoDataset Ninja:

IITM-HeTra Dataset

141831
Tagsurveillance
Taskobject detection
Release YearMade in 2018
LicenseGNU GPL 2.0
Download139 MB

Introduction #

Deepak Mittal, Avinash Reddy, Gitakrishnan Ramaduraiet al.

The authors of IITM-HeTra: Dataset for Vehicle Detection in Heterogeneous Traffic Scenarios emphasize the utility of video image processing from traffic camera feeds for tasks like counting and classifying vehicles, estimating queue length, determining traffic speed, and tracking individual vehicles. In contrast to homogeneous traffic, heterogeneous traffic involves various vehicle types that do not adhere to lane discipline, making vehicle detection particularly challenging, especially when vehicles are occluded, a common occurrence in such scenarios.

Recent advancements in Deep Learning have demonstrated significant potential in addressing various computer vision tasks, including object recognition, detection, and tracking. However, it’s worth noting that training deep learning models necessitates extensive labeled datasets, which are both time-consuming and expensive to obtain. To address this challenge, the authors propose a solution involving data augmentation. Specifically, they augment an existing large, general (non-traffic) dataset with a small, low-resolution dataset of heterogeneous traffic (collected by the authors themselves). This augmentation approach yields state-of-the-art vehicle detection performance. It’s important to highlight that, to the best of the authors’ knowledge, the dataset they collected, known as IITM-HeTra, represents the first publicly available labeled dataset for heterogeneous traffic.

To ensure that data are temporally uncorrelated, the authors of the study sampled a frame every two seconds from multiple video streams. A total of 2400 frames were extracted. 2400 frames were manually labeled under different vehicle categories by the authors. After careful scrutiny and elimination of unclear images, the number of available frames was reduced to 1417. The dataset was then divided by the authors into a trainval set (1202 images) and a test set (216 images), and this split was retained for all experiments. Initially, eight different vehicle classes commonly seen in Indian traffic were defined by the authors. Some of these classes were similar, while the two classes had fewer labeled instances; these were merged into similar-looking classes by the authors. For instance, in the dataset, there were different categories for small cars, SUVs, and sedans, which were merged under the “light motor vehicle (LMV)” category by the authors. The collected dataset contained a total of 6319 labeled vehicles. This included 3294 two-wheelers, 279 heavy motor vehicles (HMV), 2148 cars, and 598 autorickshaws. A second dataset was created by the authors by merging cars and autorickshaws together into the “light motor vehicle (LMV)” class. Approximately 25.2% of the vehicles were occluded, according to the authors.

Please note, that the original light motor vehicle (LMV) and cars classes are merged into a single cars class. heavy motor vehicles (HMV) class is renamed into bus, and two-wheelers is renamed to person respectfully.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

IITM-HeTra: Dataset for Vehicle Detection in Heterogeneous Traffic Scenarios is a dataset for an object detection task. It is used in the surveillance industry, and in the vehicle detection domain.

The dataset consists of 1418 images with 6360 labeled objects belonging to 3 different classes including person, car, and bus.

Images in the IITM-HeTra dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: trainval (1202 images) and test (216 images). The dataset was released in 2018 by the IIT Madras, India.

Dataset Poster

Explore #

IITM-HeTra dataset has 1418 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 IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
OpenSample annotation mask from IITM-HeTraSample image from IITM-HeTra
👀
Have a look at 1418 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 3 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 1-3 of 3
Class
Images
Objects
Count on image
average
Area on image
average
person
rectangle
1229
3335
2.71
3.31%
car
rectangle
1127
2746
2.44
7.68%
bus
rectangle
253
279
1.1
8.59%

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.

Search
Rows 1-3 of 3
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
3335
1.22%
4.95%
0.34%
27px
5.6%
168px
34.85%
83px
17.16%
19px
2.97%
100px
15.62%
car
rectangle
2746
3.17%
13.34%
0.35%
17px
3.53%
254px
52.7%
102px
21.24%
22px
3.44%
180px
28.12%
bus
rectangle
279
7.8%
28.67%
0.56%
25px
5.19%
355px
73.65%
172px
35.62%
52px
8.12%
268px
41.88%

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 6360 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 6360
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
person
rectangle
frame_672.jpg
482 x 640
124px
25.73%
61px
9.53%
2.45%
2
person
rectangle
frame_672.jpg
482 x 640
69px
14.32%
36px
5.62%
0.81%
3
person
rectangle
frame_672.jpg
482 x 640
71px
14.73%
30px
4.69%
0.69%
4
person
rectangle
frame_672.jpg
482 x 640
94px
19.5%
56px
8.75%
1.71%
5
person
rectangle
frame_672.jpg
482 x 640
74px
15.35%
34px
5.31%
0.82%
6
person
rectangle
frame_672.jpg
482 x 640
40px
8.3%
36px
5.62%
0.47%
7
car
rectangle
frame_80.jpg
482 x 640
130px
26.97%
73px
11.41%
3.08%
8
car
rectangle
frame_80.jpg
482 x 640
75px
15.56%
56px
8.75%
1.36%
9
person
rectangle
frame_80.jpg
482 x 640
78px
16.18%
43px
6.72%
1.09%
10
bus
rectangle
frame_953.jpg
482 x 640
59px
12.24%
102px
15.94%
1.95%

License #

IITM-HeTra: Dataset for Vehicle Detection in Heterogeneous Traffic Scenarios is under GNU GPL 2.0 license.

Citation #

If you make use of the IITM-HeTra data, please cite the following reference:

@dataset{IITM-HeTra,
  author={Deepak Mittal and Avinash Reddy and Gitakrishnan Ramadurai and Kaushik Mitra and Balaraman Ravindran},
  title={IITM-HeTra: Dataset for Vehicle Detection in Heterogeneous Traffic Scenarios},
  year={2018},
  url={https://www.kaggle.com/datasets/deepak242424/iitmhetra}
}

Source

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

@misc{ visualization-tools-for-iitm-hetra-dataset,
  title = { Visualization Tools for IITM-HeTra Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/iitm-hetra } },
  url = { https://datasetninja.com/iitm-hetra },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset IITM-HeTra 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='IITM-HeTra', 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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