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Data Brief
Data Brief
Data in Brief
2352-3409
Elsevier

S2352-3409(24)00850-3
10.1016/j.dib.2024.110887
110887
Data Article
Multi-object urban dataset: A resource for detecting pedestrians, traffic and motorbikes
Patil Kailas kailas@eng.src.ku.ac.th
kailas.patil@vupune.ac.in
@patilkr
ab⁎
Gatagat Darshana a
Rumane Omkar a
Pashankar Siddharth a
chumchu Prawit prawit@eng.src.ku.ac.th
b⁎⁎
a Vishwakarma University, Pune, India
b Kasetsart University, Sriracha, Thailand
⁎ Corresponding author at: Vishwakarma University, Pune, India. kailas@eng.src.ku.ac.thkailas.patil@vupune.ac.in@patilkr
⁎⁎ Corresponding author at: Kasetsart University, Sriracha, Thailand. prawit@eng.src.ku.ac.th
29 8 2024
12 2024
29 8 2024
57 11088728 5 2024
13 8 2024
23 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
This article describes a dataset comprising 16,426 real-world urban photographs, capturing vehicles, cyclists, motorbikes, and pedestrians across Morning, Evening, and Night scenes. The dataset is valuable for machine learning tasks in traffic analysis, urban planning, and public safety. It enables the development and validation of algorithms for pedestrian detection, traffic flow analysis, and infrastructure optimization. Our main goal is to assist academics, urban planners, and decision-makers in creating sophisticated models for pedestrian safety, traffic control, and accident avoidance. This dataset is a useful resource for training and verifying algorithms targeted at boosting real-time traffic monitoring systems, optimizing urban infrastructure, and raising overall road safety because of its high variability and significant volume. This dataset represents a major advancement for smart city projects and the creation of intelligent transportation systems.

Keywords

Object detection
Urban dataset
Pedestrians
Traffic analysis
Motorbikes
Bicycles
Public safety
Machine learning
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pmcSpecifications TableSubject	Applied Machine Learning, Object detection in autonomous vehicles.	
Specific subject area	Urban Traffic Analysis, Intelligent Transportation Systems	
Data format	Raw	
Type of data	Image (The dataset consists of images stored in JPG format with a resolution of 768 × 1024 pixels)	
Data collection	To portray real-world settings, a systematic collection of 16,426 photographs of pedestrians, motorbikes and cyclists, and traffic was made from diverse urban sites. Images were collected using an iPhone 13 with a 12 MP sensor, 26 mm focal length, and f/1.6 aperture. We captured images at three distinct times of day (morning, evening, night) across five different urban locations in Pune, Maharashtra (Bibwewadi, Sahakar Nagar, Katraj, Kondhwa, Sadashiv Peth). These locations were chosen to capture a diverse range of urban scenarios, including crowded intersections, residential areas, and commercial zones. Changes in traffic density, pedestrian activity, and artificial and natural lighting were all taken into consideration when designing this strategy. To capture these scenes, several cameras and mobile devices were used, guaranteeing a variety of angles and levels of quality. An attempt was made to obtain pictures from various viewpoints and lengths of shot in order to give a complete picture of the subjects and their interactions in the city. The photographs were carefully sorted according to when they were taken and the main subject matter of the picture, which included pedestrians using cell phones, cyclists and motorbikes, and traffic movement. To ensure uniformity and speed up processing, all photos were saved in JPG format and standardized to 768 × 1024 pixels in resolution. With the use of this dataset, machine learning models for smart city applications, traffic analysis, and safety monitoring should be able to be developed and tested with greater stability.	
Data source location	1. Bibwewadi, Pune, Maharashtra

Latitude - 18.4690° N, Longitude - 73.8641° E

2. Sahakar Nagar, Pune, Maharashtra

Latitude - 18.4756° N, Longitude - 73.8544° E

3. Katraj, Pune, Maharashtra

Latitude - 18.4529° N, Longitude −73.8652° E

4. Kondhwa, Pune, Maharashtra

Latitude - 18.4695° N, Longitude - 73.8890° E

5. Sadashiv Peth, Pune, Maharashtra

Latitude −18.5082° N, Longitude - 73.8441° E

	
Data accessibility	Repository name: UrbanScene: An Extensive Multi-Object Dataset for Pedestrian, Traffic, and Motorbike Detection
Data identification number: 10.17632/5gt4fg4rvp.1
Direct URL to data: https://data.mendeley.com/datasets/5gt4fg4rvp/1	

1 Value of the Data

The 16,426-image dataset shows pedestrians, motorbikes and cyclists, and traffic in a variety of real-world scenarios. It is an invaluable resource for several important applications and research areas, such as:• This dataset provides a comprehensive resource for training and testing machine learning models in urban traffic analysis, enhancing the accuracy of pedestrian and vehicle detection systems.

• It supports the development of intelligent transportation systems by providing real-world scenarios for the validation of autonomous vehicle algorithms. The dataset can be used to boost overall road safety by strengthening autonomous vehicles' capacity to recognize and react to bikes and pedestrians.

• The dataset aids urban planners and public safety officials in analyzing traffic patterns and pedestrian behavior, contributing to safer urban environments.

• The diversity of lighting and environmental conditions captured in the dataset enables the development of robust algorithms capable of functioning in various urban settings.

• Researchers can use this dataset to explore the interaction dynamics between different urban entities, such as vehicles and pedestrians, in varying traffic densities.

In conclusion, this dataset provides a thorough basis for a wide range of technological developments and useful applications in the field of autonomous vehicle development. For researchers, urban planners, and tech developers looking to improve the effectiveness, safety, and dependability of autonomous driving systems in practical settings, it is a valuable resource.

2 Background

Urban environments present complex challenges for traffic management, pedestrian safety, and urban planning. With the proliferation of machine learning techniques, thereʼs a growing need for diverse and comprehensive urban datasets to train and validate algorithms for real-world applications. However, existing datasets often lack variability and scale, limiting their effectiveness in addressing the complexities of urban environments. This paper addresses this gap by introducing a substantial dataset of urban imagery, enabling research and development in traffic analysis, pedestrian detection, and infrastructure optimization.

The significance and potential impact of the dataset:1. Why do we need data on pedestrian behavior and urban traffic for autonomous vehicles?

Autonomous vehicles rely on this data to train and enhance their object detection algorithms. It offers a variety of scenarios that improve these systems' capacity to safely navigate intricate urban areas by assisting them in effectively identifying and responding to different road users and conditions.

2. Why is it crucial to research pedestrian safety and traffic dynamics in relation to autonomous vehicles?

Comprehending pedestrian behavior and traffic dynamics is essential to creating autonomous systems that can anticipate and respond to real-world events.

3. What revolutionary technological advancements could these data lead to?

The dataset has the potential to propel progress in autonomous vehicle object identification technologies, resulting in more reliable and accurate recognition systems. It facilitates the creation of improved algorithms for navigation and decision-making in real-time under various illumination and environmental circumstances.

3 Data Description

The goal of this object detection dataset is to forward the research and assessment of models and algorithms for the detection of three different categories: motorbikes and cyclists, pedestrians, and traffic. This dataset is to assist studies in autonomous driving, urban planning, and public safety by offering a varied collection of photos with identified objects. The dataset consists of images stored in JPG format with a resolution of 768 × 1024 pixels.

The dataset comprises a total of 16,426 images, distributed across three categories as follows:Traffic (7227 images): includes a range of vehicle types, including trucks, buses, and cars.

Pictures show cars in a variety of traffic situations, such as chaotic, light, and free-flowing traffic.

Pedestrians (4106 images): includes pictures of people standing or strolling in a variety of settings, including sidewalks, crosswalks, and open spaces.

Motorcycles & Bicycles (5093 images): This includes pictures of motorcycles and motorbikes riding on roads, in bike lanes, and in situations with varying traffic.

Features of the Image: 1. Diverse Backgrounds: The photographs showcase a range of urban and rural environments, such as parks, residential neighborhoods, highways, and city streets.

2. Lighting: Pictures are taken in a variety of lighting scenarios, such as during the day, at night, at dusk, and in sunny, cloudy weather.

3. Camera Angles: The dataset consists of pictures shot from various perspectives, such as top-down, side, front, and angled views, which replicate real-world surveillance and on-vehicle camera setups.

4. Object Density: The range of object densities in the photos, from situations with few objects to those with plenty of overlapping objects in dense scenes.

This dataset serves multiple purposes, including enhancing the vision systems of self-driving cars to better recognize and respond to oncoming traffic, pedestrians, and two-wheeled vehicles, thereby advancing autonomous driving technology. Additionally, it supports the analysis of pedestrian movement and traffic patterns, aiding in the optimization of urban infrastructure design. Furthermore, it contributes to the development of surveillance systems aimed at monitoring and ensuring public safety in metropolitan areas, thereby bolstering efforts in the domain of public safety (Table 1).Table 1 Dataset distribution.

Table 1Sr. No	Categories	Number of images	
1	Traffic	7227	
2	Pedestrians	4106	
3	Motorbikes & Cyclists	5093	
Total	16,426	

The five locations in Pune, Maharashtra, were carefully selected to represent a broad spectrum of urban environments, including high-traffic areas, residential neighborhoods, and commercial districts. These locations were chosen to capture a wide range of urban behaviors and traffic patterns that are representative of typical Indian urban settings (Fig. 1).Fig. 1 Folder structure of multiple objects urban dataset consisting of cyclist, pedestrians, motorbikes & traffic.

Fig 1

Each category is organized within distinct folders, ensuring straightforward access and identification of specific environments (Morning, Evening, Night) in which images are taken. Table 2 shows sample images of the dataset for cyclist, pedestrian, motorbike and traffic.Table 2 Sample images of multiple objects urban dataset in different environments.

Table 2Multiple objects traffic dataset	
Categories	Cyclist	Motor-Bike	Pedestrian	Traffic	
Morning	Image, table 2	Image, table 2	Image, table 2	Image, table 2	
Evening	Image, table 2	Image, table 2	Image, table 2	Image, table 2	
Night	Image, table 2	Image, table 2	Image, table 2	Image, table 2	

4 Experimental Design, Materials and Methods

4.1 Experimental design

The Multiple Objects traffic images were captured using the Iphone 13 mobile phone, ensuring consistent image quality and resolution for each image. Fig. 2 shows the dataset consisting of multiple objects helpful for object detection. The dataset encompasses four objects (Cyclist, Motorbikes, Pedestrians, Traffic), introducing variability in environment (Morning, Evening, Night) to mimic real-world scenarios. Fig. 3, Fig. 4, Fig. 5, Fig. 6 shows how the images are being captured from different angles (Front, Back, Left & right sides) for Cyclist, Biker, Pedestrian & Traffic respectively (Fig. 7).Step 1: Image Capture (April 2024) -In this stage, we went out throughout the day, evening, and night to gather photographs relating to the situations listed in our dataset. The primary goal was to gather a comprehensive collection of photographs related to various traffic scenarios and surroundings (Fig. 8).Fig. 8 Resized image.

Fig 8

Step 2: Image Pre-processing (May 2024) -During this stage, we improved the quality of all taken photographs by resizing them to 768 × 1024 with FastStone Photo Resizer and categorising them appropriately. After preprocessing, the dataset was trained in three distinct models to demonstrate its worth. Respectively, we received the predicted outcomes.

Fig. 2 Multi-object urban dataset consisting of pedestrian, cyclist, biker & traffic.

Fig 2

Fig. 3 Cyclist images captured from different angles.

Fig 3

Fig. 4 Biker images captured from different angles.

Fig 4

Fig. 5 Pedestrian images captured from different angles.

Fig 5

Fig. 6 Traffic images captured from different angles.

Fig 6

Fig. 7 Data acquisition process.

Fig 7

4.2 Materials or specification of image acquisition system

The mobile phone (iPhone 13) used in the data acquisition process and the specifications of the captured images are:Sensor Type: 12 MP

Focal Length: 26 mm

Aperture: f/1.6

Aspect Ratio: 4:3

The photographs were saved in the jpg format and then resized to a resolution of 768 × 1024 pixels with FastStone Photo Resizer. These specs include important information about the cameras utilized and the picture attributes collected throughout the data collection procedure.

4.3 Preprocessing method

In our research, we used FastStone Photo Resizer as the first step in picture preprocessing. FastStone Photo Resizer is a robust image processing programme that is often used for batch image resizing. Fig. 9 summarizes the procedure. FastStone Photo Resizer simplifies the resizing process for large batches of photographs, making it an important tool for preprocessing in a variety of research applications, such as image-based machine learning, image analysis, and data augmentation.Fig. 9 Preprocessing steps of images in the dataset.

Fig 9

4.4 Evaluation of machine learning models on the dataset

In the domain of machine learning datasets, numerous significant contributions have arisen in recent times [[2], [3], [4], [5], [6], [7], [8], [9], [10], [11]], addressing various machine learning applications. Using the Multi-Object Urban dataset [1], which includes pedestrians, cyclists, bicyclists, and general traffic, we investigated the possibilities of this dataset to address a number of research topics related to machine learning and object recognition for autonomous vehicles to improve driving. We specifically looked into the performance of DenseNet 210, Xception, and InceptionV3, three well-known pre-trained machine learning models. These models are well-known for their outstanding image recognition performance.

We standardized the photos in our preprocessing pipeline by downsizing them to a uniform 128 × 128 pixel resolution. It is noteworthy to mention that we utilized the built-in ′preprocess_inputʼ function of the Keras framework for the preprocessing step. This function ensures that the input data meets the requirements and expectations of the model and is specifically designed for each pretrained model. The models were optimized using the “Adam” optimizer, which trained them over a total of five epochs.

Our findings indicated that training these models with our Multi-Object Urban dataset led to enhanced accuracy across all three models, compared to when they were not trained on our dataset. DenseNet 201 improved to 99.15 % accuracy from 42.12 % previously, Xception to 97.63 % accuracy from 8.98 %, and InceptionV3 to 96.65 % accuracy from 26.84.We found that the DenseNet 201 model provides the best accuracy for our dataset. These findings emphasize the value of the dataset for researchers who want to maximize the driving of autonomous vehicles by utilizing machine learning algorithms to identify objects in traffic. The accuracy of pre-trained models before and after training is summarized in Table 3.Table 3 Comparison of pretrained model performance with and without training on our dataset.

Table 3Model	Accuracy (Before Training)	Accuracy (After Training)	
DenseNet 201	42.12 %	99.15 %	
Xception	8.98 %	97.63 %	
InceptionV3	26.84 %	96.65 %	

With regard to our four-class classification problem including the categories of “Pedestrian,” “Cyclist,” “Motorbike,” and “Traffic,” the previously mentioned confusion matrix in Table 4 provides a thorough analysis of the modelʼs predicted accuracy. It enables us to discover the modelʼs strong points and weak points, allowing us to find true positives—instances within each class that the model properly identifies—and forecasted positives and predicted negatives—where the model falters. This detailed assessment is essential for interpreting the behavior of the model and directing enhancements.Table 4 Confusion Matrix of pretrained models with and without Training on our Dataset.

Table 4No	Model	Confusion Matrix (Before Training)	Confusion Matrix (After Training)	
1	DenseNet 201	Image, table 4	Image, table 4	
2	Xception	Image, table 4	Image, table 4	
3	InceptionV3	Image, table 4	Image, table 4	

The dataset is useful for Integration into Autonomous Vehicle Navigation Systems. Our future work is on integrating this dataset into autonomous vehicle systems to improve object detection algorithms. The datasetʼs diverse urban scenarios provide an excellent resource for refining how autonomous vehicles detect and respond to pedestrians, cyclists, and motorbikes, particularly in complex urban environments.

Additionally, the dataset can contribute to the design and implementation of smart city infrastructure, such as intelligent crosswalks that detect pedestrian traffic and adjust signal timings accordingly. This would enhance pedestrian safety and ensure smoother traffic flow, especially in densely populated areas.

Limitations

While offering a diverse array of urban imagery, this dataset may have limitations stemming from its geographical coverage and the specific environmental conditions depicted, potentially constraining its relevance to certain regions or scenarios. While the dataset is geographically limited to Pune, the selected locations provide a microcosm of general urban behavior in similar mid-sized cities in India, making the dataset valuable for broader urban traffic analysis.

Dataset Availability

The dataset is publicly available [1] on Mendeley Data under the repository name “UrbanScene: An Extensive Multi-Object Dataset for Pedestrian Traffic and Motorbike Detection” with a doi:10.17632/5gt4fg4rvp.1

Ethics Statement

Our study does not involve studies with animals or humans. Therefore, we confirm that our research strictly adheres to the guidelines for authors provided by Data in terms of ethical considerations. The authors assert that there are no conflicts of interest. The dataset's authors are portrayed in the dataset images. Privacy has been maintained during image capture.

CRediT Author Statement

Kailas Patil: Conceptualization, Supervision, Writing – review & editing. Darshana Gatagat: Writing – review & editing. Omkar Rumane: Methodology, Siddharth Pashankar: Methodology. Prawit Chumchu: Conceptualization, Methodology, Writing – review & editing.

Data Availability

UrbanScene: An Extensive Multi-Object Dataset for Pedestrian, Traffic, and Motorbike Detection (Original data) (Mendeley Data).

Acknowledgments

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We are grateful to Kasetsart University Sriracha Campus, Thailand and Vishwakarma University, Pune, India for their support and provision of necessary resources during this research endeavour.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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