DocumentCode :
1910352
Title :
Pedestrians detection using a cascade of LBP and HOG classifiers
Author :
Cosma, Claudiu ; Brehar, Raluca ; Nedevschi, Sergiu
Author_Institution :
Comput. Sci. Dept., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania
fYear :
2013
fDate :
5-7 Sept. 2013
Firstpage :
69
Lastpage :
75
Abstract :
Accurate pedestrian detection in urban environment is a highly explored research field. We propose a new approach in pedestrian detection that combines the popular Local Binary Patterns and Histogram of Oriented Gradient features. The novelty of our work resides in the combination of a reduced HOG feature vector with uniform LBP patterns for the pedestrian data representation. Another contribution resides in the design and implementation of a two-stage cascade classifier of Support Vector Machine. Our method has been trained and tested on reference benchmark datasets and it proved to have good results.
Keywords :
data structures; feature extraction; image classification; pedestrians; support vector machines; traffic information systems; HOG classifiers; HOG feature vector; LBP classifiers; histogram of oriented gradient features; local binary pattern features; pedestrian data representation; pedestrian detection; reference benchmark datasets; support vector machine; two-stage cascade classifier; uniform LBP patterns; urban environment; Accuracy; Feature extraction; Histograms; Support vector machine classification; Testing; Training; HOG; SVM; pedestrians LBP;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computer Communication and Processing (ICCP), 2013 IEEE International Conference on
Conference_Location :
Cluj-Napoca
Print_ISBN :
978-1-4799-1493-7
Type :
conf
DOI :
10.1109/ICCP.2013.6646084
Filename :
6646084
Link To Document :
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