Comparison of support vector machines and deep learning for vehicle detection

Küçük Resim Yok

Tarih

2018

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

CEUR-WS

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

The main goal of this paper is to compare different Vehicle Detection algorithms and to provide an effective comparison technique for developers and researchers. During this study, fine tunings are suggested to improve the implementations of these algorithms. Our focus on Support Vector Machines (SVM) and then Deep Learning based approaches. The SVM based vehicle detection implementation utilizes Histogram Oriented Gradients (HOG). The deep learning approach we consider is the YOLO implementation. Our evaluation employs 400 random frames extracted from a real world driving video. As stated by the experimental results, YOLO is more accurate with %81.9 success than SVM which only scored %57.8. © 2018 CEUR-WS.

Açıklama

3rd International Conference on Recent Trends and Applications in Computer Science and Information Technology, RTA-CSIT 2018 -- 23 November 2018 through 24 November 2018 -- -- 143396

Anahtar Kelimeler

Kaynak

CEUR Workshop Proceedings

WoS Q Değeri

Scopus Q Değeri

N/A

Cilt

2280

Sayı

Künye