Title :
Automatic detection of bicycle direction using RealAdaBoost and C4.5
Author :
Nakata, H. ; Hirokane, M.
Author_Institution :
Kansai Univ., Kansai, Japan
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;
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
DOI :
10.1109/SCIS-ISIS.2012.6505150
