Automatic Motorcycle Detection on Public Roads

Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. Over the past years, automated mechanisms to inspect traffic violations such as radars and surveillance cameras are being used ever more. This paper’s goals are the study and implementation of some methods for automatic detection of motorcycles on public roads. Traffic images captured by cameras were used. For feature extraction of images, the algorithms SURF, HAAR, HOG and LBP were used as descriptors. For image classification, Multilayer Perceptron, Support Vector Machines and Radial-Bases Function Networks were used as classifiers. Finally, the results are presented and discussed.

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Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.00236 ◽

2010 ◽

Vol 30 (1) ◽

pp. 236-239 ◽

Cited By ~ 1

Author(s):

Liang XU

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Support Vector Machines ◽

Chemical Process ◽

Least Square ◽

Support Vector ◽

Vector Machines ◽

Nonlinear Feature Extraction ◽

Nonlinear Feature

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Support vector machines combined with wavelet-based feature extraction for identification of drugs hidden in anthropomorphic phantom

Measurement ◽

10.1016/j.measurement.2012.06.016 ◽

2013 ◽

Vol 46 (1) ◽

pp. 284-293 ◽

Cited By ~ 8

Author(s):

Dingjun Qu ◽

Wei Li ◽

Yu Zhang ◽

Bai Sun ◽

Yu Zhong ◽

...

Keyword(s):

Feature Extraction ◽

Support Vector Machines ◽

Support Vector ◽

Anthropomorphic Phantom ◽

Vector Machines

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Support vector machines for histogram-based image classification

IEEE Transactions on Neural Networks ◽

10.1109/72.788646 ◽

1999 ◽

Vol 10 (5) ◽

pp. 1055-1064 ◽

Cited By ~ 879

Author(s):

O. Chapelle ◽

P. Haffner ◽

V.N. Vapnik

Keyword(s):

Support Vector Machines ◽

Image Classification ◽

Support Vector ◽

Vector Machines

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Application of Support Vector Machines to Airborne Hyper-Spectral Image Classification

Lecture Notes in Electrical Engineering - Advances in Neural Network Research and Applications ◽

10.1007/978-3-642-12990-2_50 ◽

2010 ◽

pp. 439-444 ◽

Cited By ~ 1

Author(s):

Ming-Der Yang ◽

Kai-Siang Huang ◽

Ji-Yuan Lin ◽

Pei Liu

Keyword(s):

Support Vector Machines ◽

Image Classification ◽

Support Vector ◽

Spectral Image ◽

Hyper Spectral ◽

Vector Machines

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An Estimation of PCA Feature Extraction in EEG-based Emotion Prediction with Support Vector Machines

Proceedings of Data Analytics and Management - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-981-16-6289-8_53 ◽

2022 ◽

pp. 651-664

Author(s):

M. Malathi ◽

G. Aloy Anuja Mary ◽

J. Senthil Kumar ◽

P. Sinthia ◽

M. Nalini

Keyword(s):

Feature Extraction ◽

Support Vector Machines ◽

Support Vector ◽

Vector Machines

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MULTI-VIEW GENDER CLASSIFICATION USING MULTI-RESOLUTION LOCAL BINARY PATTERNS AND SUPPORT VECTOR MACHINES

International Journal of Neural Systems ◽

10.1142/s0129065707001317 ◽

2007 ◽

Vol 17 (06) ◽

pp. 479-487 ◽

Cited By ~ 47

Author(s):

HUI-CHENG LIAN ◽

BAO-LIANG LU

Keyword(s):

Feature Extraction ◽

Support Vector Machines ◽

Local Binary Pattern ◽

Spatial Information ◽

Local Binary Patterns ◽

Support Vector ◽

Gender Classification ◽

Classification Rate ◽

Vector Machines ◽

Facial Images

In this paper, we present a novel method for multi-view gender classification considering both shape and texture information to represent facial images. The face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. Following the idea of local binary pattern, we propose a new feature extraction approach called multi-resolution LBP, which can retain both fine and coarse local micro-patterns and spatial information of facial images. The classification tasks in this work are performed by support vector machines (SVMs). The experiments clearly show the superiority of the proposed method over both support gray faces and support Gabor faces on the CAS-PEAL face database. A higher correct classification rate of 96.56% and a higher cross validation average accuracy of 95.78% have been obtained. In addition, the simplicity of the proposed method leads to very fast feature extraction, and the regional histograms and fine-to-coarse description of facial images allow for multi-view gender classification.

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