An Initial Deep CNN Design Approach for Identification of Vehicle Color and Type for Amber and Silver Alerts

Pillai, Uma K. Kesava; Valles, Damian

doi:10.1109/ccwc51732.2021.9375917

Cited by 5 publications

(6 citation statements)

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“…Data management and big data issuesManaging big data in the transportation and other VDC‐related field faces many challenges including data generation, transmission, and development of services. These issues can be partially addressed by meta‐data repositories, open data platforms, and cross‐system data sharing 7,70 Security and privacySimilar to other areas, security and privacy are among the most important issues in the VCD field.…”

Section: Discussionmentioning

confidence: 99%

“…Managing big data in the transportation and other VDC‐related field faces many challenges including data generation, transmission, and development of services. These issues can be partially addressed by meta‐data repositories, open data platforms, and cross‐system data sharing 7,70 …”

Section: Discussionmentioning

confidence: 99%

“…There are various approaches to perform vehicle classification including rule‐based methods, 15 machine‐learning methods, 2,36,42,68–80 data mining methods, 73 soft computing methods, 7 and specific methods 5,26 . A summary of the vehicle classification methods is shown in Figure 9.…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

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A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Hashemi

Emami

Sangar

2021

Int J Communication

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Summary The vehicle detection and classification (VDC) problem has received much attention recently due to the increased security threats and the need to develop intelligent transportation systems. A large number of approaches have been proposed for the VDC problem using neural networks. To determine how neural networks‐based approaches have developed for the VDC in recent years, this paper surveys the VDC approaches through a literature review with the range Jan. 2012 through Apr. 2021. To do this, we introduce a new comparison framework to classify and compare the VDC approaches. Our proposed framework is composed of nine comparison dimensions: input data type, vehicle type, scale, scope, dynamicity, vehicle detection method, vehicle classification method, application, and evaluation method. Next, using the proposed framework, we discuss the evolution of the VDC approaches and identify several open issues that have emerged in the field. This paper provides a guide for researchers to use or design robust VDC systems with proper characteristics based on their needs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Hashemi

Emami

Sangar

2021

Int J Communication

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“…Variants of CNN networks are widely used in CV studies in the field of ITS. There are a number of CNN-based studies in the literature, such as those focused on automatic license plate recognition [ 40 , 41 ], traffic sign detection and recognition [ 25 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], vehicle detection [ 52 , 53 , 54 , 55 ], pedestrian detection [ 56 , 57 , 58 , 59 , 60 ], lane line detection [ 61 , 62 , 63 ], obstacle detection [ 64 ], video anomaly detection [ 65 , 66 , 67 , 68 ], structural damage detection [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ], and steering angle detection [ 79 , 80 , 81 , 82 ] in autonomous vehicles. The most popular and advanced CNN-based architectures in the literature [ 83 , 84 ] are presented in Figure 3 .…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

“…In addition, vehicle classification is actively used in smart transportation systems, as well as fleet tracking and parking systems. Among the models developed for vehicle classification, models developed using DL techniques [ 54 , 161 , 162 ] occupy a large part of the literature.…”

Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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