Vehicle Recognition Using Contourlet Transform and SVM

Rahati, Saeed; Moravejian, R.; Mohamad, Ebtesam A.; Mohamad, Fatma Susilawati

doi:10.1109/itng.2008.136

Cited by 36 publications

(17 citation statements)

References 7 publications

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“…There are however approaches where reported success rate is higher than in case of the iCamera system, but either number of predicted models is much less, as in [43] and [60] or other conditions, e.g., in which test images were acquired, make the classification task easier, as in [57]. Compared to the architecture presented in this paper, the MMR system described in [34] reports a better average success rate which is over 98 %.…”

Section: System Efficiencymentioning

confidence: 70%

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A smart camera for the surveillance of vehicles in intelligent transportation systems

Baran

Ruść

Fornalski

2015

Multimed Tools Appl

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The paper presents a smart camera aimed at security and law enforcement applications for intelligent transportation systems. An extended background is presented first as a scholar literature review. The smart camera components and their capabilities for automatic detection and recognition of selected parameters of cars, as well as different aspects of the system efficiency, are described and discussed in detail in subsequent sections. Smart features of make and model recognition (MMR), license plate recognition (LPR) and color recognition (CR) are highlighted as the main benefits of the system. Their implementations, flowcharts and recognition rates are described, discussed and finally reported in detail. In addition to MMR, three different approaches, referred to as bag-of-features, scalable vocabulary tree and pyramid match, are also considered. The conclusion includes a discussion of the smart camera system efficiency as a whole, with an insight into potential future improvements.

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Section: System Efficiencymentioning

confidence: 70%

“…According to results reported in [35], SVN gives better results when combined with DCT, especially when the SVM one-against-one strategy is used. Similar research based on the contourlet transform [22] is presented in [60].…”

Section: Literature Reviewmentioning

confidence: 85%

A smart camera for the surveillance of vehicles in intelligent transportation systems

Baran

Ruść

Fornalski

2015

Multimed Tools Appl

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“…[3] used Curvelet transform features with a Support Vector Machine (SVM) in another proposed solution. [4] proposed Contourlet transform features in conjunction with SVM classifier. [5] proposed an improvement to the technique proposed by [4].…”

Section: Introductionmentioning

confidence: 99%

“…[4] proposed Contourlet transform features in conjunction with SVM classifier. [5] proposed an improvement to the technique proposed by [4]. They proposed the use of a localized Contourlet feature extraction technique as opposed to the use of standard deviations of the Contourlet coefficients.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Make and Model Recognition in CCTV footage

Saravi

Edirisinghe

2013

2013 18th International Conference on Digital Signal Processing (DSP)

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“…Also, Kazemi et al [7] used contourlet to recognize the type of car. In their method, instead of using direct contourlet coefficients they used standard deviation.…”

Section: Introductionmentioning

confidence: 99%

Car type recognition in highways based on wavelet and contourlet feature extraction

Jamzad

2010

2010 International Conference on Signal and Image Processing

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Recently many works focus on the vehicle type recognition because it is important in security and authentication systems. Computational complexity and low recognition rate especially when the system has to recognize among a large number of vehicles, are two major problems in vehicle type recognition. In recent years wavelet and contourlet transform have been applied in the recognition tasks successfully. In this paper we proposed a method for recognizing vehicle type in different lighting conditions. We used wavelet and contourlet as tools for feature extraction. These features are powerful and robust to illumination and scale variation. We reduced the dimension of feature vector by resizing the wavelet and contourlet subbands and then applied normalization on those coefficients. Our method is robust to a few variations in vehicle frontal view angels and distance to camera. The experimental results showed 97.35% true recognition rate for 14 classes of cars which is a significant increase for vehicle type recognition.

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Vehicle Recognition Using Contourlet Transform and SVM

Cited by 36 publications

References 7 publications

A smart camera for the surveillance of vehicles in intelligent transportation systems

A smart camera for the surveillance of vehicles in intelligent transportation systems

Vehicle Make and Model Recognition in CCTV footage

Car type recognition in highways based on wavelet and contourlet feature extraction

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