Vehicle Logo Recognition by Spatial-SIFT Combined with Logistic Regression

An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT features, Support Vector Machine and K-Nearest Neighbours classifiers. By testing with the largest vehicle logo data-set, it is shown that the proposed framework can achieve a classification accuracy of 99.93%, the best among all studied methods. Moreover, the proposed framework shows robustness when noise is added in both training and testing images

Pengenalan Citra Logo Kendaraan Menggunakan Metode Gray Level Co-Occurence Matrix (Glcm) dan Jst-Backpropagation

A car is a vehicle that has a varied shape or model but the difference is the brand or logo. Vehicle logos have their own meaning and meaning for car industry companies. The logo should have a practical and effective or efficient function so that the logo form is part of the marketing and branding program of the car industry company [1]. There are three types of car logos that are now known, in the form of symbols, text, or a combination between the two. The logo is always in the front and back of the car body and usually has a lighter color than the color of the vehicle. One that supports the development of technology is how to recognize a vehicle either from the brand, shape, model and color of the vehicle. Some references that are deemed feasible to help this research include utilizing the weaknesses and weaknesses of the results of previous research, including a paper entitled. Scale Invariant Feature Transform (SIFT) [2]. SIFT is combined with Logistic Regression [3] based on Gradient Orientation Histogram (HOG). Logo Recognition Using Probabilistic Neural Networks [4]. Therefore, the researchers wanted to focus on the logo recognition using the extraction of the Gray Level Co-occurrence Matrix (GLCM) feature. Testing and training testing using ANN-Backpropagation. From the results of this study the best accuracy obtained 95.7%, so that GLCM and ANN-Backpropagation can recognize the image of the vehicle logo.DOI : 10.29408/jit.v1i1.89

Online Vehicle Logo Recognition Using Cauchy Prior Logistic Regression

Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches typically consider training models on large datasets. However, there might only be a small training dataset to start with and more images can be obtained during the real-time applications. This paper proposes an online image recognition framework which provides solutions for both small and large datasets. Using this recognition framework, models are built efficiently using a weight updating scheme. Another novelty of this work is that the Cauchy prior logistic regression with conjugate gradient descent is proposed to deal with the multinomial classification tasks. The Cauchy prior results in a quicker convergence speed for the weight updating process which could decrease the computational cost for both online and offline methods. By testing with a publicly available dataset, the Cauchy prior logistic regression deceases the classification time by 59%. An accuracy of up to 98.80% is achieved when the proposed framework is applied

Online Vehicle Logo Recognition Using Cauchy Prior Logistic Regression

Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches typically consider training models on large datasets. However, there might only be a small training dataset to start with and more images can be obtained during the real-time applications. This paper proposes an online image recognition framework which provides solutions for both small and large datasets. Using this recognition framework, models are built efficiently using a weight updating scheme. Another novelty of this work is that the Cauchy prior logistic regression with conjugate gradient descent is proposed to deal with the multinomial classification tasks. The Cauchy prior results in a quicker convergence speed for the weight updating process which could decrease the computational cost for both online and offline methods. By testing with a publicly available dataset, the Cauchy prior logistic regression deceases the classification time by 59%. An accuracy of up to 98.80% is achieved when the proposed framework is applied

Machine Learning Methods for Autonomous Object Recognition and Restoration in Images

Image recognition and image restoration are important tasks in the field of image processing. Image recognition are becoming very popular due to the state-of-the-art deep learning methods. However, these models usually require big datasets and high computational costs, which could be challenging. This thesis proposes an online learning framework that deals with both small and big datasets. For small datasets, a Cauchy prior logistic regression classifier is proposed to provide a quick convergence, and the online weight updating scheme is efficient due to the previously trained weights being reused. For big datasets, convolutional neural network could be implemented. For image recognition, non-parametric classifiers are often used for image recognition such as K-nearest neighbours, however, K-nearest neighbours are vulnerable to noise and high dimensional features. This thesis proposes a non-parametric classifier based on Bayesian compressive sensing; the developed classifier is robust and it does not need a training stage. For image restoration, which is usually performed before image recognition as a preprocessing process. This thesis proposes such a joint framework that performs image recognition and restoration simultaneously. In image restoration, image rotation and occlusion are common problems but convolutional neural networks are not suitable to solve these due to the limitation of the convolutional process and pooling process. This thesis develops a joint framework based on capsule networks. The developed joint capsule framework could achieve a good result on recognition, image de-noising, recovering rotation and removing occlusion. The developed algorithms have been evaluated for vehicle logo restoration and recognition, however, they are transferable to other implementations. This thesis also developed an automatic detection and recognition framework for badger monitoring for the first time. Badger plays a key role in the transmission of bovine tuberculosis, which is described by government as the most pressing animal health problem in the UK. An automatic badger monitoring system could help researcher to understand the transmission mechanisms and thereby to develop methods to deal with the transmission between species

Vehicle logo classification using bag of word descriptor and support vector machine classifier

Intelligent Transportation Systems play an important role in traffic areas such as to record vehicular traffic data. In order to improve transportation safety and security, a system with the ability to automatically extract and recognize a vehicle is needed apart from the existing plate number recognition system. The detection and recognition of the vehicle type or model can be helpful in determining whether the vehicle is registered with the department of motor vehicle. Hence, this project aims at providing extra information with respect to the vehicle which is to determine the maker of the vehicles. In this project, the classification system is trained with 10 training images for each vehicle’s manufacturer. The common features for each logo will be extracted using the Speeded-Up Robust Features algorithm and then feature points will be grouped and arranged using Bag of Word representations which will then be clustered using K means clustering method. The vehicle’s classification will be determined by using Support Vector Machine classifier to classify and identify the logo of the vehicle. From the experimental results, the classification system achieved 87% and 77% for front view and side view images respectively with 1500, number of cluster

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras

A nonparametric Bayesian compressive sensing classification

This paper presents a novel non-parametric back-propagation Bayesian compressive sensing (BBCS) classification approach. While the state-of-the-art parametric classifiers such as logistic regression require model training and can result in inadequate models, the developed approach does not require model training. It is combined with a column-based subspace sampling process and it can deal efficiently with uncertainties and highly computational tasks. Validation on a publicly available vehicle logo dataset shows that the proposed classifier can achieve up to 98% recognition accuracy as compared with the state-of-the-art non-parametric classifiers. Compared with the generic Bayesian compressive sensing classification, the proposed approach decreases the mean number of misclassifications by 87% and with 68% reduction of the computational time. The robustness of the BBCS approach is demonstrated over scene recognition tasks, and its outperformance over the AlexNet convolutional neural networks algorithm is demonstrated in noisy conditions. The proposed BBCS approach is generic and can be used in different areas, for example, it has shown robustness over the CIFAR-10 dataset

Deliverable D1.4 Visual, text and audio information analysis for hypervideo, final release

Having extensively evaluated the performance of the technologies included in the first release of WP1 multimedia analysis tools, using content from the LinkedTV scenarios and by participating in international benchmarking activities, concrete decisions regarding the appropriateness and the importance of each individual method or combination of methods were made, which, combined with an updated list of information needs for each scenario, led to a new set of analysis requirements that had to be addressed through the release of the final set of analysis techniques of WP1. To this end, coordinated efforts on three directions, including (a) the improvement of a number of methods in terms of accuracy and time efficiency, (b) the development of new technologies and (c) the definition of synergies between methods for obtaining new types of information via multimodal processing, resulted in the final bunch of multimedia analysis methods for video hyperlinking. Moreover, the different developed analysis modules have been integrated into a web-based infrastructure, allowing the fully automatic linking of the multitude of WP1 technologies and the overall LinkedTV platform