RETRACTED ARTICLE: Feature selection and classification methods for vehicle tracking and detection

Kumar, C. Ranjeeth; Ray, Anuradha

doi:10.1007/s12652-020-01824-3

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…An open problem is to examine the different VDC systems under a unified benchmarking process to discover their weaknesses and strengths and select the most effective approach for a specific application. Some researchers have evaluated all the components of a VDC system, while others have focused only on one specific component 56,57,107 …”

Section: Discussionmentioning

confidence: 99%

“…The systems that work on small scales are more complicated and cannot be applied to large‐scale domains. Medium‐scale: This type of system has medium complexity and is appropriate for medium operation domains. Some medium‐scale VDC systems are given in previous works 55–57 Large‐scale : This type of VDC system has a large operation domain, less complexity, and high adaptability. …”

Section: Our Proposed Frameworkmentioning

confidence: 99%

“…Machine learning methods are one of the robust tools for vehicle classification 6,29,44,56,62,83,91–93 and different areas such as IoV, 42,44,45,91,94 vehicular cloud, 95,96 and vehicular networks 97–102 . Rather than attempt to fully simulate the decision process of an expert or best practice, machine learning methods typically only take the outcomes from the experts.…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

“…Precision ( P ) is the ratio of correctly predicted observations to the total predicted positive observations 56

P = \frac{TP}{italicTP + italicFP} .

…”

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: Our Proposed Frameworkmentioning

confidence: 99%

Section: Our Proposed Frameworkmentioning

confidence: 99%

“…Precision ( P ) is the ratio of correctly predicted observations to the total predicted positive observations 56

P = \frac{TP}{italicTP + italicFP} .

…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Furthermore, depending on the labels, FS algorithms can be categorized into three groups: unsupervised, semi‐supervised, and supervised 30 . Supervised FS is applicable for single‐label (SL) 22,31 and multi‐labels (ML) 28,32–36 . Features relationship specified by assessing features communication with the class.…”

Section: Introductionmentioning

confidence: 99%

Ensemble feature selection for multi‐label text classification: An intelligent order statistics approach

Miri

Dowlatshahi

Hashemi

et al. 2022

Int J of Intelligent Sys

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Because of the overgrowth of data, especially in text format, the value and importance of multi‐label text classification have increased. Aside from this, preprocessing and particularly intelligent feature selection (FS) are the most important step in classification. Each FS finds the best features based on its approach, but we try to use a multi‐strategy approach to find more useful features. Evaluating and comparing features’ importance and relevance makes using multiple strategy and methods more suitable than conventional approaches because each feature is measured based on several perspectives. Nevertheless, the ensemble FS merges the final performance results of various methods to take advantage of different methods’ strengths and better classify. In this article, we have proposed an ensemble FS method for multi‐label text data (MLTD) for the first time using the order statistics (EMFS) approach. We have utilized four multi‐label FS (MLFS) algorithms with various particular performances to achieve a good result. In this method, as one of the most important statistics methods, Order Statistics was used to aggregate the ranks of different algorithms, which is robust against noise, redundant and inessential features. In the end, the performance of EMFS, executing six MLTDs, was evaluated according to six performance criteria (ranking‐based and classification‐based). Surprisingly, the proposed method was more accurate than others among all used MLTDs. The proposed method has improved by 1.5% compared to other methods. This value is based on the results obtained based on six evaluation criteria and all tested data sets.

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A Video-Based System for Vehicle Tracking Based on Optical Flow and Shi-Tomasi Corner Detection Algorithm

Patel

Brahmbhatt²

2023

Lecture Notes in Networks and Systems

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RETRACTED ARTICLE: Feature selection and classification methods for vehicle tracking and detection

Cited by 13 publications

References 19 publications

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

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

Ensemble feature selection for multi‐label text classification: An intelligent order statistics approach

A Video-Based System for Vehicle Tracking Based on Optical Flow and Shi-Tomasi Corner Detection Algorithm

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