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

Jamzad, Mansour

doi:10.1109/icsip.2010.5697497

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Vehicle type classification is also explored by using vehicle geographical features [ 19 ], edge-based features [ 20 ], histogram of gradient (HoG) features [ 21 ], contour point features [ 22 ], curvelet transform features [ 23 ], and contourlet transform features [ 24 ]. Some studies combined two features, such as wavelet and contourlet features, to improve results [ 25 ], as well as PHOG and Gabor features [ 26 ]. Dong et al.…”

Section: Related Workmentioning

confidence: 99%

A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition

Hayee,

Hussain,

Yousaf

2023

Sensors

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Vehicle make and model recognition (VMMR) is an important aspect of intelligent transportation systems (ITS). In VMMR systems, surveillance cameras capture vehicle images for real-time vehicle detection and recognition. These captured images pose challenges, including shadows, reflections, changes in weather and illumination, occlusions, and perspective distortion. Another significant challenge in VMMR is the multiclass classification. This scenario has two main categories: (a) multiplicity and (b) ambiguity. Multiplicity concerns the issue of different forms among car models manufactured by the same company, while the ambiguity problem arises when multiple models from the same manufacturer have visually similar appearances or when vehicle models of different makes have visually comparable rear/front views. This paper introduces a novel and robust VMMR model that can address the above-mentioned issues with accuracy comparable to state-of-the-art methods. Our proposed hybrid CNN model selects the best descriptive fine-grained features with the help of Fisher Discriminative Least Squares Regression (FDLSR). These features are extracted from a deep CNN model fine-tuned on the fine-grained vehicle datasets Stanford-196 and BoxCars21k. Using ResNet-152 features, our proposed model outperformed the SVM and FC layers in accuracy by 0.5% and 4% on Stanford-196 and 0.4 and 1% on BoxCars21k, respectively. Moreover, this model is well-suited for small-scale fine-grained vehicle datasets.

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Section: Related Workmentioning

confidence: 99%

A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition

Hayee,

Hussain,

Yousaf

2023

Sensors

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“…Model-based MMR [11,12] could deliver very high accuracy; however, it is inapplicable in time-sensitive applications or online recognition. Additionally, some studies [2,13] combined multiple features to gain higher recognition accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…MMR can also be used with offline and online systems to discover fraudulent license plates by checking the registration database. Major highway traffic can also be monitored by computing the travel time for each vehicle traveling between two cameras [2]. It can also be used in automated vehicle parking systems to further optimize the parking space by the knowledge of MMR [1].…”

Section: Introductionmentioning

confidence: 99%

Recognition of vehicle make and model in low light conditions

2020

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This paper presents a method for vehicle make and model recognition (MMR) in low lighting conditions. While many MMR methods exist in the literature, these methods are designed to be used only in perfect operating conditions. The various camera configuration, lighting condition, and viewpoints cause variations in image quality. In the presented method, the vehicle is first detected, image enhancement is then carried out on the detected front view of the vehicle, followed by features extraction and classification. The performance is then examined on a low-light dataset. The results show around 6% improvement in the ability of MMR with the use of image enhancement over the same recognition model without image enhancement.

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“…In previous techniques, feature-based approaches have been commonly used, such as geographical feature [1], edge-based feature [2,3], histogram of gradient (HoG) feature [4,5] contour point feature [6], curvelet transform feature [7], and contourlet transform feature [8,9]. In addition, some works used combinations of two features in order to gain better results, for example, integration of wavelet and contourlet features [10], and combination of PHOG and Gabor features [11].…”

Section: Related Work and Contributions 21 Related Workmentioning

confidence: 99%

Car make and model recognition under limited lighting conditions at night

Boonsim

Prakoonwit

2016

Pattern Anal Applic

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Car make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when license plate numbers cannot be identified or fake number plates are used. CMMR can also be used when a certain model of a vehicle is required to be automatically identified by cameras. The majority of existing CMMR methods are designed to be used only in daytime when most of the car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. The aim of this work was to identify car make and model at night by using available rear view features. This paper presents a one-class classifier ensemble designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and license plates from the rear view is extracted and used in the recognition process. The majority vote from support vector machine, decision tree, and k-nearest neighbors is applied to verify a target model in the classification process. The experiments on 421 car makes and models captured under limited lighting conditions at night show the classification accuracy rate at about 93 %.

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

Cited by 9 publications

References 8 publications

A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition

A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition

Recognition of vehicle make and model in low light conditions

Car make and model recognition under limited lighting conditions at night

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