An Off the Shelf CNN Features Based Approach for Vehicle Classification Using Acoustics

Bansal, Anam; Aggarwal, Naveen; Vij, Dinesh; Sharma, Akashdeep

doi:10.1007/978-3-030-00665-5_110

Cited by 5 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After all, the classified vehicles are ready to be used in different domains and applications such as VANETs, 7 IoV, 8 intelligent transportation, 10,36–38 traffic congestion avoidance, 4,32 navigation systems, 39–41 traffic/vehicular surveillance, 1,16 accident prevention, 33–35,37 insurance (rating), 2 unmanned aerial vehicle control, 4,10,36,38 and communication systems, 42–45 among others.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Also, these methods need a sequence of images to detect vehicles 23 . The main objective is to separate the moving foreground objects from the background in the input sources 2 . Motion‐based methods are appropriate for real‐time applications such as traffic analysis.…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

“…This serious task is referred to as vehicle detection and classification (VDC). The VDC aims to recognize vehicles, extract a summary of important information about them, and classify vehicles into some groups 2–4 . VDC is a central task to realize intelligent transportation systems (ITS), 5,6 vehicular ad hoc networks (VANETs), 7 and Internet of Vehicles (IoV) 8 .…”

Section: Introductionmentioning

confidence: 99%

“…These approaches can be categorized into five main groups: License plate‐based VDC: This is the most common approach for recognizing and classifying vehicles. This approach processes license plates and compares the results with the corresponding records in the precollected databases 2,9 . However, the performance is not perfect because of the distortion, change, or blocking of a plate, which in turn diminishes the performance of vehicle classification.…”

Section: Introductionmentioning

confidence: 99%

“…Vehicle logo detection has been considered due to its various applications such as vehicle monitoring and brand reputation measurement. Logo‐based approaches face some challenges, including complex and different shapes, illumination, scale changes, background disturbance, low object resolutions, rich variations in logo contents, and the low detection accuracy for complex vehicle logos 2,10 Appearance‐based VDC: In this approach, different characteristics of vehicles are used for the detection process.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Hashemi

Emami

Sangar

2021

Int J Communication

View full text Add to dashboard Cite

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.

show abstract