A Comparative Study of Vehicle Detection Methods in a Video Sequence

Chetouane, Ameni; Mabrouk, Sabra; Jemili, Imen; Mosbah, Mohamed

doi:10.1007/978-3-030-40131-3_3

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…These methods identify vehicles based on their appearance characteristics such as edge, slope, corner, and vehicle size. Appearance‐based methods can identify vehicles without motion information and can be used to correctly identify moving vehicles from the image sequences 64 . These methods can detect a wide range of vehicles depending on the features under consideration 65 .…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

“…The wide variation in the appearance of vehicles, the high similarity between vehicles, the camera vision domain, and the variation in the illumination limit the performance of appearance‐based methods in real‐world applications. To address these challenges, some methods use histogram features such as Haar‐like features and histogram of oriented gradient 64 . To design robust systems, vehicle detection methods should be equipped with machine learning techniques that are trained with different models.…”

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: Our Proposed Frameworkmentioning

confidence: 99%

Section: Our Proposed Frameworkmentioning

confidence: 99%