Integrating appearance and edge features for on-road bicycle and motorcycle detection in the nighttime
Journal
2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
Pages
354-359
Date Issued
2014
Author(s)
Abstract
It is critical to detect bicycles and motorcycles on the road because collision of autos with those light vehicles becomes major cause of on-road accidents nowadays especially in the nighttime. Therefore, a vision-based nighttime bicycle and motorcycle detection method relying on use of a camera and near-infrared lighting mounted on an auto vehicle is proposed in this paper. Generally, the foreground objects in front of the auto, not the far-away background, will reflect near-infrared lighting in the nighttime environments. However, some components of the bicycles and the motorcycles absorb most infrared lighting and thus make the bicycles and motorcycles hardly recognizable. To cope with this problem, the aforementioned detection method is part-based, which combines the two kinds of features related to the characteristics of bicycles and motorcycles. Also, the information about the geometric relation among all the parts and the object centroid is learned off-line. Due to high computation load, Adaboost algorithm is used to select effective parts with better geometric information for detection. To validate the proposed results, several experiments are conducted to show that the developed system is reliable in detecting bicycles and motorcycles in the nighttime. © 2014 IEEE.
Event(s)
2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
Subjects
Feature Integration; Nighttime; On-road Bicycle and Motorcycle Detection
SDGs
Other Subjects
Accidents; Adaptive boosting; Bicycles; Infrared devices; Intelligent systems; Intelligent vehicle highway systems; Lighting; Motorcycles; Roads and streets; Sporting goods; AdaBoost algorithm; Computation loads; Feature integration; Foreground objects; Geometric information; Geometric relations; Motorcycle detection; Nighttime; Feature extraction
Type
conference paper