Part‐based recognition of vehicle make and model

Biglari, Mohsen; Soleimani, Ali; Hassanpour, Hamid

doi:10.1049/iet-ipr.2016.0969

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…Biglari et al [1,24] present a part-based method that tries to find the discriminating parts for each vehicle sub-group. In [1], mention that using CNN neural networks requires heavy computations, large memory, and time.…”

Section: B the Methods Based On Feature Extraction And Applying To Th...mentioning

confidence: 99%

A Novel Approach for Vehicle Identification based on Image Registration and Deep Learning

Asgarian Dehkordi,

Khosravi,

Asgarian Dehkordi

et al. 2022

Scientia Iranica

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Fine-grained vehicle type recognition using on-road cameras is among interesting topics in machine vision. It has several challenges like inter-class similarity, different viewing angles, and different lighting and weather conditions. This paper presents a novel approach for vehicle classification based on a novel augmentation method and deep learning. In the proposed smart augmentation, the vehicle images of each class are registered on the reference vehicles of all other classes and then added to the training set of that class. In this way, we will have a lot of new images which are very similar to both reference and target classes. This helps the CNN model to handle inter-class similarities very well. In the test phase, the input image is registered on every reference image in parallel and applied to the model. Finally, the winner is determined by summing up the provided scores of all models. The targeted data augmentation along with the proposed classification strategy has high recognition power and is capable of providing high accuracy using small CNNs or any other classification method without the need for large datasets. The proposed method achieved a recognition rate of 99.8% with only 150K parameters.

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Section: B the Methods Based On Feature Extraction And Applying To Th...mentioning

confidence: 99%

A Novel Approach for Vehicle Identification based on Image Registration and Deep Learning

Asgarian Dehkordi,

Khosravi,

Asgarian Dehkordi

et al. 2022

Scientia Iranica

View full text Add to dashboard Cite

show abstract

“…Biglari et al [4] have presented a part-based approach that tries to find distinctive parts for each subgroup of vehicles. They pointed out that the use of deep CNN networks requires heavy computing, a lot of memory and time.…”

Section: Vehicle Identification Methodsmentioning

confidence: 99%

“…These includes vehicle speed estimation [1,2], dimensions approximation [3], make and model recognition [4,5], and orientation estimation [6]. The main challenge for estimating these parameters is camera calibration.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Speed and Dimensions Estimation from On-Road Cameras by Identifying Popular Vehicles

2020

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One of the major issues related to traffic monitoring systems is analyzing the behavior of vehicles and identifying their characteristics. In this paper, an automated algorithm is proposed for calibration of the road cameras. This calibration is used to estimate the speed and dimensions of the passing vehicles. In this method, a motion plane is obtained in the initial frames according to the direction of moving vehicles. After modeling the background, removing shadows, and identifying the exact boundaries of passing vehicles, a number of common vehicles are recognized by the Bag of Words method. Given the actual metric dimensions of these vehicles and the equivalent dimensions on the motion plane, the metric coefficients are calculated and the calibration process is completed. Each passing vehicle is projected on the motion plane, and speed and dimensions are calculated by tracking it along the road. To test the accuracy of the proposed method, we constructed a ground-truth video dataset, by simultaneous capturing the road vehicles using RGB and speed cameras. Furthermore, to identify common vehicles, a dataset of vehicle images was collected. The proposed method evaluated on our dataset and the mean error of 1.15 km/h is achieved. Accordingly, it outperforms previous methods.

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“…A composite deep-structure classifier was built using multiple classifiers of samples generated in the target scene based on a confidence score with a voting mechanism. The authors in [ 28 ] adopted latent SVM in a vehicle make and model recognition. A novel greedy parts localization algorithm was employed to extract some descriptive parts in the vehicles used in the learning stages.…”

Section: Introductionmentioning

confidence: 99%

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

Yousef

Flora

Iftekharuddin

2022

Sensors

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The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients (HOG) are extracted from the segmented traffic. Furthermore, a multi-class support vector machine classifier is employed to categorize different traffic vehicle types, including passenger cars, passenger trucks, motorcycles, buses, and small and large utility trucks. It handles multiple vehicle detections through an iterative k-means clustering over-segmentation process. The proposed algorithm reduced the processed data by an average of 40%. Compared to recent techniques, it showed an average improvement of 15% in segmentation accuracy, and it is 55% faster than the compared segmentation techniques on average. Moreover, a comparative analysis of 23 different deep learning architectures is presented. The resulting algorithm outperformed the compared deep learning algorithms for the quality of vehicle classification accuracy. Furthermore, the timing analysis showed that it could operate in real-time scenarios.

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Part‐based recognition of vehicle make and model

Cited by 12 publications

References 31 publications

A Novel Approach for Vehicle Identification based on Image Registration and Deep Learning

A Novel Approach for Vehicle Identification based on Image Registration and Deep Learning

Vehicle Speed and Dimensions Estimation from On-Road Cameras by Identifying Popular Vehicles

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

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