Car recognition from frontal images in mobile environment

Varjas, Viktor; Tanács, Attila

doi:10.1109/ispa.2013.6703849

Cited by 20 publications

(10 citation statements)

References 7 publications

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“…Edges based features are explored in [20][21][22][23][24]. In these approaches, dependence on edges can lead to failure of the system due to occlusion.…”

Section: Vehicle Make and Model Recognitionmentioning

confidence: 99%

“…The authors use the small and simplistic dataset to evaluate the proposed system. Vajas et al [22] also use concatenated SMG for global representation and Clady et al [23] use concatenated oriented contour points from Sobel edges. Both Vajas and Clady use Nearest Neighbors as a classifier for their proposed VMMR system.…”

Section: Vehicle Make and Model Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Vehicle Make and Model Recognition System

Manzoor

Morgan

Bais

2019

MAKE

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A Vehicle Make and Model Recognition (VMMR) system can provide great value in terms of vehicle monitoring and identification based on vehicle appearance in addition to the vehicles’ attached license plate typical recognition. A real-time VMMR system is an important component of many applications such as automatic vehicle surveillance, traffic management, driver assistance systems, traffic behavior analysis, and traffic monitoring, etc. A VMMR system has a unique set of challenges and issues. Few of the challenges are image acquisition, variations in illuminations and weather, occlusions, shadows, reflections, large variety of vehicles, inter-class and intra-class similarities, addition/deletion of vehicles’ models over time, etc. In this work, we present a unique and robust real-time VMMR system which can handle the challenges described above and recognize vehicles with high accuracy. We extract image features from vehicle images and create feature vectors to represent the dataset. We use two classification algorithms, Random Forest (RF) and Support Vector Machine (SVM), in our work. We use a realistic dataset to test and evaluate the proposed VMMR system. The vehicles’ images in the dataset reflect real-world situations. The proposed VMMR system recognizes vehicles on the basis of make, model, and generation (manufacturing years) while the existing VMMR systems can only identify the make and model. Comparison with existing VMMR research demonstrates superior performance of the proposed system in terms of recognition accuracy and processing speed.

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“…Edges based features are explored in [20][21][22][23][24]. In these approaches, dependence on edges can lead to failure of the system due to occlusion.…”

Section: Vehicle Make and Model Recognitionmentioning

confidence: 99%

Section: Vehicle Make and Model Recognitionmentioning

confidence: 99%

Real-Time Vehicle Make and Model Recognition System

Manzoor

Morgan

Bais

2019

MAKE

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show abstract

“…Pearce and Pears [2] concatenate the Square-Mapped Gradients (SMG) or Locally Normalised Harris Strengths (LNHS) as global feature vectors for the images. Varjas and Tanacs [19] also used concatenated SMG. The SMG-based techniques require well-aligned ROIs with strictly frontal views, or planar projection of skewed views onto frontal-like views.…”

Section: B Features Extraction and Global Features Representationmentioning

confidence: 99%

“…A kNN-based classification scheme was also used by Varjas and Tanacs [19], but with a correlation-based distance metric. In these approaches, accuracy is degraded when ROIs are even slightly different than ground truth ROIs.…”

Section: Classification Approachesmentioning

confidence: 99%

Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features

Siddiqui

Mammeri

Boukerche

2016

IEEE Trans. Intell. Transport. Syst.

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In this paper, we propose and evaluate unexplored approaches for real-time automated vehicle make and model recognition (VMMR) based on a bag of speeded-up robust features (BoSURF) and demonstrate the suitability of these approaches for vehicle identification systems. The proposed approaches use SURF features of vehicles' front-or rear-facing images and retain the dominant characteristic features (codewords) in a dictionary. Two schemes of dictionary building are evaluated: "single dictionary" and "modular dictionary." Based on the optimized dictionaries, the SURF features of vehicles' front-or rear-face images are embedded into BoSURF histograms, which are used to train multiclass support vector machines (SVMs) for classification. Two real-time VMMR classification schemes are proposed and evaluated: a single multiclass SVM and an ensemble of multiclass SVM based on attribute bagging. The processing speed and accuracy of the VMMR system are affected greatly by the size of the dictionary. The tradeoff between speed and accuracy is studied to determine optimal dictionary sizes for the VMMR problem. The effectiveness of our approaches is demonstrated through cross-validation tests on a recent publicly accessible VMMR data set. The experimental results prove the superiority of our work over the state of the art, in terms of both processing speed and accuracy, making it highly applicable to real-time VMMR systems.

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“…The corner points of the extended QR code region are detected as intersection points of the lines fitted to the set of contour points of the segmented mask. [12] The projection transformation is determined based on these corner points assuming that the original pattern is a square and distortions are due to the varying positioning and angles of the camera taking the picture. Then the image can be rectified using the found transformation ( Fig.…”

Section: Image Processingmentioning

confidence: 99%

Fast Recognition of Natural Feature Identifiers by a Mobile Phone

Katona¹,

Nyúl²

2015

Acta Cybern

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One of the most important and widely used technique for automatic identification is the use of visual codes. Identifiers encoded in various symbols and patterns make electronic reading possible, that greatly helps and speeds up processing, e.g., at cashier lines, warehouse transactions, high speed processing places, production lines. The common codes designed using geometric patterns usually identify types or entities. However, patterns can be produced that, by their nature, are unique and thus can be used to validate originality or authenticity. In this paper, we focus on the automatic localization and recognition of a kind of natural feature identifier (NFI). We present an image processing algorithm that successfully locates NFI code region in an image taken by a mobile camera and extracts features of the NFI glitters that can be the basis for recognition. We also show preliminary experimental results.

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Car recognition from frontal images in mobile environment

Cited by 20 publications

References 7 publications

Real-Time Vehicle Make and Model Recognition System

Real-Time Vehicle Make and Model Recognition System

Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features

Fast Recognition of Natural Feature Identifiers by a Mobile Phone

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