Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study

Intelligent transportation systems have been very well received by car companies, people, and governments around the world. The main challenge in the world of smart and self-driving cars is to identify obstacles, especially pedestrians, and take action to prevent collisions with them. Many studies in this field have been done by various researchers, but there are still many errors in the accurate detection of pedestrians in self-made cars made by different car companies, so in the research in this study, we focused on the use of deep learning techniques to identify pedestrians for the development of intelligent transportation systems and self-driving cars and pedestrian identification in smart cities, and then some of the most common deep learning techniques used by various researchers were reviewed. Finally, in this research, the challenges in each field are discovered, which can be very useful for students who are looking for an idea to do their dissertations and research in the field of smart transportation and smart cities.

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Explainable AI in Machine/Deep Learning for Intrusion Detection in Intelligent Transportation Systems for Smart Cities

10.1201/9781003172772-17 ◽

2021 ◽

pp. 297-321

Author(s):

Andria Procopiou ◽

Thomas M. Chen

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Intelligent Transportation Systems ◽

Smart Cities ◽

Intelligent Transportation ◽

Transportation Systems ◽

Explainable Ai

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Ant-Inspired Recurrent Deep Learning Model for Improving the Service Flow of Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2020.3037902 ◽

2020 ◽

pp. 1-10

Author(s):

Gunasekaran Manogaran ◽

Mamoun Alazab

Keyword(s):

Deep Learning ◽

Intelligent Transportation Systems ◽

Intelligent Transportation ◽

Learning Model ◽

Transportation Systems ◽

Service Flow ◽

Deep Learning Model

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Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Electronics ◽

10.3390/electronics10101136 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1136

Author(s):

David Augusto Ribeiro ◽

Juan Casavílca Silva ◽

Renata Lopes Rosa ◽

Muhammad Saadi ◽

Shahid Mumtaz ◽

...

Keyword(s):

Deep Learning ◽

Image Quality ◽

Intelligent Transportation Systems ◽

Light Field ◽

State Of The Art ◽

Intelligent Transportation ◽

Transportation Systems ◽

Machine Learning Algorithms ◽

Learning Framework ◽

Reconstruction Methods

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

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A review on vision-based pedestrian detection in intelligent transportation systems

Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control ◽

10.1109/icnsc.2012.6204951 ◽

2012 ◽

Cited By ~ 12

Author(s):

Bo Li ◽

Qingming Yao ◽

Kunfeng Wang

Keyword(s):

Intelligent Transportation Systems ◽

Pedestrian Detection ◽

Intelligent Transportation ◽

Transportation Systems

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An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

Future Internet ◽

10.3390/fi13120306 ◽

2021 ◽

Vol 13 (12) ◽

pp. 306

Author(s):

Ahmed Dirir ◽

Henry Ignatious ◽

Hesham Elsayed ◽

Manzoor Khan ◽

Mohammed Adib ◽

...

Keyword(s):

Deep Learning ◽

Intelligent Transportation Systems ◽

Smart Cities ◽

Weather Conditions ◽

Research Area ◽

Transportation Systems ◽

Detection Accuracy ◽

Adverse Weather ◽

Object Counting ◽

The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

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