Object classification using CNN for video traffic detection system

Author

Hyeok Jang ; Hun-Jun Yang ; Dong-Seok Jeong ; Hun Lee

Author_Institution

Dept. of Electron. Eng., Inha Univ., Incheon, South Korea

fYear

2015

fDate

28-30 Jan. 2015

Firstpage

1

Lastpage

4

Abstract

Recently, a lot of research on the use of big data is made, and this paper was aimed to perform classification experiments using CNN for the detected object collected from traffic detectors. In addition the experimental results were compared with the HOG descriptor that is commonly used in existing pedestrian and object classification and wavelet, texture and descriptor that are used in the road surface condition classification. According to the results after applied to the collected RVFTe-10 data, the performances of HOG SVM and CNN were excellent by showing 99.9% and 99.5% respectively.

Keywords

image classification; neural nets; object detection; traffic engineering computing; CNN; HOG SVM; RVFTe-10 data; big data; convolution neural network; object classification; object detection; road surface condition classification; support vector machine; video traffic detection system; Accuracy; Data models; Feature extraction; Histograms; Neural networks; Support vector machines; Wavelet transforms; Convolution Neural Network; Object classification; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers of Computer Vision (FCV), 2015 21st Korea-Japan Joint Workshop on

Conference_Location

Mokpo

Type

conf

DOI

10.1109/FCV.2015.7103755

Filename

7103755