Vehicle detection for intelligent traffic surveillance system

Abid, Nesrine; Ouni, Tarek; Abid, Mohamed

doi:10.1109/atsip49331.2020.9231936

“…As for Wen et al [32] studied, the authors also suggested the method based on Haar-like features with AdaBoost algorithm but with a slight change introducing an improved normalization algorithm for vehicle detection. Apart from that, Abid et al [19] presented an image detection technique based on multi-scale covariance (MSCOV) descriptor for image description and support vector machine (SVM) classifier for vehicle classification. Support vector machine (SVM) is a superior classifier in classifier-based vehicle verification method where two-group image classifiers are implemented to distinguish the vehicle from non-vehicle [29].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review

Yusup¹,

Wang²,

Lin³

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Population growth in large cities has contributed to the increase in vehicles' number, leading to the traffic congestion problem. Incompetent traffic supervision could squander an inconsiderable number of man-hours and might lead to fatal consequences. Therefore, intelligent traffic surveillance systems have to carry more significant roles in highway monitoring and traffic management system throughout the years. Although vehicle detection and classification methods have evolved rapidly throughout the years, they still lack high-level reasoning. Accurate and precise vehicle recognition and classification are still insufficient to develop an intelligent and reliable traffic system. There is a demand to increase the confidence in image understanding and effectively extract the images conformed to human perception and without human interference. This paper attempts to summarize a review on several methods that semantically extract and analyze traffic density with image processing techniques. Three (3) methods that have been selected to be discussed in this paper are semantic analysis of traffic video using image understanding, mining semantic context details of traffic scene, and integrating vision and language in semantic description of traffic events from image sequences. Each method is discussed thoroughly, and their outstanding issue is deliberated in this paper.

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“…As for Wen et al [32] studied, the authors also suggested the method based on Haar-like features with AdaBoost algorithm but with a slight change introducing an improved normalization algorithm for vehicle detection. Apart from that, Abid et al [19] presented an image detection technique based on multi-scale covariance (MSCOV) descriptor for image description and support vector machine (SVM) classifier for vehicle classification. Support vector machine (SVM) is a superior classifier in classifier-based vehicle verification method where two-group image classifiers are implemented to distinguish the vehicle from non-vehicle [29].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review

Yusup

¹

,

Wang

²

,

Lin

³

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

0

View full text Add to dashboard Cite

Population growth in large cities has contributed to the increase in vehicles' number, leading to the traffic congestion problem. Incompetent traffic supervision could squander an inconsiderable number of man-hours and might lead to fatal consequences. Therefore, intelligent traffic surveillance systems have to carry more significant roles in highway monitoring and traffic management system throughout the years. Although vehicle detection and classification methods have evolved rapidly throughout the years, they still lack high-level reasoning. Accurate and precise vehicle recognition and classification are still insufficient to develop an intelligent and reliable traffic system. There is a demand to increase the confidence in image understanding and effectively extract the images conformed to human perception and without human interference. This paper attempts to summarize a review on several methods that semantically extract and analyze traffic density with image processing techniques. Three (3) methods that have been selected to be discussed in this paper are semantic analysis of traffic video using image understanding, mining semantic context details of traffic scene, and integrating vision and language in semantic description of traffic events from image sequences. Each method is discussed thoroughly, and their outstanding issue is deliberated in this paper.

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A Comparative Study of Artificial Intelligence based Vehicle Classification Algorithms used to Provide Smart Mobility

Prarthana¹,

Hegde²,

Sushmitha³

et al. 2022

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)

0

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Vehicle detection for intelligent traffic surveillance system

Cited by 3 publications

References 20 publications

A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review

A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review

A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review

A Comparative Study of Artificial Intelligence based Vehicle Classification Algorithms used to Provide Smart Mobility

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