An efficient selection of HOG feature for SVM classification of vehicle

Lee, Seung–Hyun; Bang, Min-Suk; Jung, Kyu Won; Yi, Kang

doi:10.1109/isce.2015.7177766

Cited by 42 publications

(17 citation statements)

References 5 publications

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“…In the literature, there are many selected and extracted features [7,9,10,32,44,45,46] such as: wave length, mean, variance, peak, valley, acreage, acoustic signals, Histogram Oriented Gradients (HOG) features, the vehicle length, Grey-Level Co-occurrence matrix features, low level features, area, width, height, centroid, and bounding box. In the classification stage, these features are employed to classify the vehicles into several classes; the most used are small, medium, and large.…”

Section: Related Workmentioning

confidence: 99%

“…In the classification stage, these features are employed to classify the vehicles into several classes; the most used are small, medium, and large. Since 2006, SVM has been used for vehicle classification using other input spaces, and other different scenarios, such as static images [47], vehicles on road ramps [10], visual surveillance from low-altitude airborne platforms [7], on-road camera [32], static side-road camera [48], and laser intensity image without vehicle occlusion [46]. Also, in this work we focus on traffic surveillance with only a vision camera as sensor, the scenarios are multilane ways with a relative high traffic load, under different weather conditions and a variable occlusion index (see [49]).…”

Section: Related Workmentioning

confidence: 99%

“…Numerous techniques are available for automatic classification of vehicles, the most commonly used being deterministic methods [26,27], stochastic methods [20,28], artificial neural networks [29,30,31], and Support Vector Machine (SVM) [7,9,10,32]. …”

Section: Introductionmentioning

confidence: 99%

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Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Velazquez-Pupo

Sierra-Romero

Torres-Roman

et al. 2018

Sensors

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This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Velazquez-Pupo

Sierra-Romero

Torres-Roman

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…In [16] is shown how the overall prediction time can be decreased, exploiting the fact that for positive (car) images, the HOG, represented in 8 bins, is symmetrical. Because the dimension of the feature vector is reduced to half, this also reduces (a little) the HOG computation time, as well as the classifier time.…”

Section: Related Workmentioning

confidence: 99%

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Ilas

2018

Computers

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In this paper we introduce a new histogram computation method to be used within the histogram of oriented gradients (HOG) algorithm. The new method replaces the arctangent with the slope computation and the classical magnitude allocation based on interpolation with a simpler algorithm. The new method allows a more efficient implementation of HOG in general, and particularly in field-programmable gate arrays (FPGAs), by considerably reducing the area (thus increasing the level of parallelism), while maintaining very close classification accuracy compared to the original algorithm. Thus, the new method is attractive for many applications, including car detection and classification.

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“…In recent years, SVM classifier with HOG features are the most popular techniques for vehicle detection [19]. In real time implementation which is important for advanced driver assistance systems applications.…”

Section: Introductionmentioning

confidence: 99%

Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

Kumar

Kishore

2017

IJECE

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Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job. Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices.

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An efficient selection of HOG feature for SVM classification of vehicle

Cited by 42 publications

References 5 publications

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

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