Vehicle Make and Model classification system using bag of SIFT features

Manzoor, Muhammad Asif; Morgan, Yasser

doi:10.1109/ccwc.2017.7868475

Cited by 29 publications

(11 citation statements)

References 3 publications

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“…The performance and computational cost of [40,41] are sufficient for VMMR work but less than what has been presented here. The work proposed in [42] does not provides satisfactory results on the NTOU-MMR dataset. The performance and computational cost of [25,33] on the NTOU-MMR dataset are acceptable but far behind from the proposed approach in terms of average accuracy and processing speed.…”

Section: Comparison With State-of-the-artmentioning

confidence: 89%

Vehicle Make and Model Recognition using Bag of Expressions

Jamil

Hussain

Yousaf

et al. 2020

Sensors

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Vehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.

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Section: Comparison With State-of-the-artmentioning

confidence: 89%

Vehicle Make and Model Recognition using Bag of Expressions

Jamil

Hussain

Yousaf

et al. 2020

Sensors

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“…In case of global features, all the features are simply concatenated to create an image feature vector. Local features based VMMR system are reported in our previous works [5,34]. There is no significant performance gain (recognition rate and processing speed) of using only local features.…”

Section: Feature Extraction and Representationmentioning

confidence: 94%

“…However, more challenging and diverse challenges are associated with VMMR as compared to other problems. Few of the challenges are listed below [5]:…”

Section: Introductionmentioning

confidence: 99%

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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“…Image Classification is considered as a computer vision branch where images are categorized [1].Image can be classified into one of the two broad categories they are Supervised and Unsupervised techniques. Image classification have been extensively studied for various purposes in literature such as in [2] performs the classification of vehicle make and model using SVM which uses a combination of SIFT and bag-of-words. X. Li and X. Guo [3] developed a forward vehicle detection system that uses HOG descriptor and SVM.…”

Section: Introductionmentioning

confidence: 99%

Categorized Image Classification uing CNN Features with ECOC Framework

Fatima¹,

Seshashayee²

2019

IJRTE

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Image Classification technique is used to classify images into categories. In this study, an application is presented to examine category based image classification by combining Support Vector Machine with error correcting output codes (ECOC) framework. The ResNet50 used as Network architecture, our image dataset include caltech101 images from 9 categories (classes) which builds our classification task a multiclass problem. ECOC is a commonly used framework to model multiclass classification problem. We present one-verses-all coding design of ECOC and apply to SVM classifier. A pre-trained CNN (convolution neural network) is used for extracting image feature and as a classifier Multiclass Support Vector Machine is used. The extracted features are then passed for classification via ECOC approach. The final classification result predicts the class labels. The application is implemented in Matlab using pre-trained CNN. The prediction accuracy of each category is evaluated and presented. The experimental result shows an accuracy of 97.6%. Further experiments are carried out on different dataset which showed that best accuracy is achieved using CNN with ECOC for multiclass problem.

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Vehicle Make and Model classification system using bag of SIFT features

Cited by 29 publications

References 3 publications

Vehicle Make and Model Recognition using Bag of Expressions

Vehicle Make and Model Recognition using Bag of Expressions

Real-Time Vehicle Make and Model Recognition System

Categorized Image Classification uing CNN Features with ECOC Framework

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