Vehicle model recognition using geometry and appearance of car emblems from rear view images

Llorca, David Fernández; Colás, D.; Daza, Iván García; Parra, I.; Sotelo, Miguel Ángel

doi:10.1109/itsc.2014.6958187

Cited by 39 publications

(17 citation statements)

References 20 publications

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“…1) Methods Limited to Frontal/Rear Images of Vehicles: There is a multitude of papers [22]- [29] using a common approach: they detect the license plate (as a common landmark) on the vehicle and extract features from the area around the license plate as the front/rear parts of vehicles are usually discriminative.…”

Section: B Fine-grained Recognition Of Vehiclesmentioning

confidence: 99%

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Sochor

Špaňhel

Herout

2019

IEEE Trans. Intell. Transport. Syst.

118

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In this paper, we focus on fine-grained recognition of vehicles mainly in traffic surveillance applications. We propose an approach that is orthogonal to recent advancements in finegrained recognition (automatic part discovery, bilinear pooling). Also, in contrast to other methods focused on fine-grained recognition of vehicles, we do not limit ourselves to a frontal/rear viewpoint, but allow the vehicles to be seen from any viewpoint. Our approach is based on 3D bounding boxes built around the vehicles. The bounding box can be automatically constructed from traffic surveillance data. For scenarios where it is not possible to use precise construction, we propose a method for an estimation of the 3D bounding box. The 3D bounding box is used to normalize the image viewpoint by "unpacking" the image into a plane. We also propose to randomly alter the color of the image and add a rectangle with random noise to a random position in the image during the training of Convolutional Neural Networks. We have collected a large fine-grained vehicle dataset BoxCars116k, with 116k images of vehicles from various viewpoints taken by numerous surveillance cameras. We performed a number of experiments which show that our proposed method significantly improves CNN classification accuracy (the accuracy is increased by up to 12 percentage points and the error is reduced by up to 50 % compared to CNNs without the proposed modifications). We also show that our method outperforms stateof-the-art methods for fine-grained recognition.

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Section: B Fine-grained Recognition Of Vehiclesmentioning

confidence: 99%

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Sochor

Špaňhel

Herout

2019

IEEE Trans. Intell. Transport. Syst.

118

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“…This area is searched by means of a sliding window approach generating many regions that are described by Histogram of Gradients (HOG) and classified by a linear Support Vector Machine (SVM). The authors also extend the research to try to find vehicle models [4]. In that paper, they limit the model search to only those of the previously recognized brand.…”

Section: Related Workmentioning

confidence: 99%

Classification of vehicle make and model with MobileNets

Wille¹,

Carnieri²,

Borba³

et al. 2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Video monitoring generates large amounts of raw data from which relevant information can be extracted using image processing techniques. When these cameras are used in tolls or for traffic monitoring it is interesting to acquire characteristics like color, license plate, make and model of the vehicles passing by. This work proposes the use of a recent class of deep learning models called MobileNets on the task of retrieving the make and model information of vehicle images. The usage of these types of models can lower computational cost and improve classification accuracy. The CompCars dataset is used to assess the accuracy of the proposed method on the task of retrieving cars make and model. Results show an improvement of 2.5% on the top-1 accuracy if compared to that reported in the extended CompCars work. Moreover, it is shown that, by means of the variations of MobileNets architectures, one can obtain the desired trade-off between complexity (computational cost) and accuracy. This is an effective approach to set up the system to match the application's requirements.

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“…Using their dataset, we prove that the performance of our approaches is superior. Certain works, such as [21], use the positions and sizes of car emblems (model symbol, trim level, etc.) and HOG features of emblem regions to classify vehicle models, assuming the make is known.…”

Section: B Features Extraction and Global Features Representationmentioning

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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Vehicle model recognition using geometry and appearance of car emblems from rear view images

Cited by 39 publications

References 20 publications

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Classification of vehicle make and model with MobileNets

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

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