Automatic detection of bicycle direction using RealAdaBoost and C4.5

Author

Nakata, H. ; Hirokane, M.

Author_Institution

Kansai Univ., Kansai, Japan

fYear

2012

fDate

20-24 Nov. 2012

Firstpage

1898

Lastpage

1902

Abstract

In recent years, there has been a decrease in the number of traffic accidents and deaths due to the improved vehicle safety and legislation related to traffic violations. However, the number of injuries is still large, with injuries incurred by bicyclists and pedestrians accounting for approximately 25% of the total number of injuries. Measures must be taken to ensure the safety of pedestrians and bicyclists, which is an important issue. This study, which proposes a system for automatically detecting the direction in which a bicycle is moving, is a contribution to the development of an overall support system for safe automobile driving.

Keywords

automobiles; bicycles; decision trees; learning (artificial intelligence); object detection; pedestrians; road accidents; road safety; road traffic; C4.5; RealAdaBoost; automobile driving safety; bicycle direction automatic detection; bicyclist safety; pedestrian safety; traffic accidents; traffic violations; vehicle safety; C4.5; RealAdaBoost; bicycle; cascade structure;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on

Conference_Location

Kobe

Print_ISBN

978-1-4673-2742-8

Type

conf

DOI

10.1109/SCIS-ISIS.2012.6505150

Filename

6505150