Vehicle Recognition Using Curvelet Transform and Thresholding

Kazemi, Farhad; Pourreza, Hamid Reza; Moravejian, R.; Kazemi, Ehsan

doi:10.1007/978-1-4020-8741-7_26

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Another vehicle recognition system proposed by [38] uses image's curvelet transform and standard deviation of curvelet coefficient matrix in different scales for feature extraction. Curvelets having time-frequency localization properties show a high degree of directionality and anisotropy.…”

Section: Vehicular Feature Based Methodsmentioning

confidence: 99%

Performance Analysis of Vehicle Detection Techniques: A Concise Survey

Hanif¹,

Mansoor

Imran³

2018

Advances in Intelligent Systems and Computing

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Attention towards Intelligent Transportation System (ITS) has increased manifold especially due to prevailing security situation in the past decade. An integral part of ITS is video-based surveillance systems extracting real-time traffic parameters such as vehicle counting, vehicle classification, vehicle velocity etc. using stationary cameras installed on road sides. In all these systems, robust and reliable detection of vehicles is significantly a critical step. Since, several vehicle detection techniques exist, evaluating these techniques with respect to different environment conditions and application scenarios will give a better choice for actual deployment. The paper presents a concise survey of vehicle detection techniques used in diverse applications of video-based surveillance systems. Moreover, three main detection algorithms; Gaussian Mixture Model (GMM), Histogram of Gradients (HoG), and Adaptive motion Histograms based vehicle detection are implemented and evaluated for performance under varying illumination, traffic density and occlusion conditions. The survey provides a ready-reference for preferred vehicle detection technique under different applications.

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Section: Vehicular Feature Based Methodsmentioning

confidence: 99%

Performance Analysis of Vehicle Detection Techniques: A Concise Survey

Hanif¹,

Mansoor

Imran³

2018

Advances in Intelligent Systems and Computing

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“…Ji et al [13] report classification performances between 93% and 95% when using a partial Gabor filter bank to represent sedan, van, hatchback, bus and truck vehicle categories. A maximum 92% classification rate is obtained by Kazemi et al [14] for the classification of five vehicle types, namely Peugeot 206, Peugeout 405, Pride, Renault 5 and Peykan, using fast curvelet transform features and a k-nearest-neighbor classifier.…”

Section: Related Workmentioning

confidence: 99%

Biologically-Inspired Visual Attention Features for a Vehicle Classification Task

Creţu

Payeur

2011

International Journal on Smart Sensing and Intelligent Systems

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The continuous rise in the number of vehicles in circulation brings an increasing need for automatically and efficiently recognizing vehicle categories for multiple applications such as optimizing available parking spaces, balancing ferry loads, planning infrastructure and managing traffic, or servicing vehicles. This paper explores the use of human visual attention mechanisms to identify a set of features that allows for fast automated classification of vehicles based on images taken from 6 viewpoints. Salient visual features classified with a series of binary support vector machines and complemented by a dissimilarity score achieve average classification rates between 94% and 97.3% for five-category vehicle classification depending on the combination of viewpoints used. The viewpoints that make the most important contribution to the classification are identified in order to decrease the implementation cost. The evaluation of performance against other feature descriptors and various

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Vehicle Identification using Eigenvehicles

Shahin

Alruily

2019

2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)

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Vehicle Recognition Using Curvelet Transform and Thresholding

Cited by 4 publications

References 10 publications

Performance Analysis of Vehicle Detection Techniques: A Concise Survey

Performance Analysis of Vehicle Detection Techniques: A Concise Survey

Biologically-Inspired Visual Attention Features for a Vehicle Classification Task

Vehicle Identification using Eigenvehicles

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