Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study. (4th October 2021)

• Record Type: Journal Article
• Authors: He, Fen; Karami Olia, Paria; Jamili Oskouei, Rozita; Hosseini, Morteza; Peng, Zhihao; BaniRostam, Touraj
• Other Names: Han Chunjia Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2021(2021)
• Publication Date: 2021-10-04
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2021/5549111
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : 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.