Paper Info
Title
Frequency-Tuned Nighttime Brake-Light Detection
Abstract
Advanced safety vehicle (ASV) is a critical issue in recent years for automobiles, especially when the number of vehicles is growing rapidly world wide. The cost down of general cameras makes it feasible to having an intelligent system of visual-based event detection in front for forward collision avoidance and mitigation. When driving in nighttime, vehicles in front are generally visible by their tail and brake lights. The brake lights are particularly important due to their consequent events that drivers need to focus on. Therefore, in this paper, we propose a novel approach that can detect brake lights at night using a camera by analyzing the signal in both spatial and frequency domain. Unlike the traditional approaches that employ the knowledge of the heuristic features, such as symmetry and position of rear facing vehicle, size and so forth, we focus on finding the invariant features from the regions of brake lights in frequency domain and therefore can conduct the detection process in a part-based manner. Experiment from extensive dataset shows that our proposed system can efficiently and effectively detect brake lights under different lighting and traffic conditions, and thus prove its feasibility in real-world environments.
Year
DOI
Venue
2010
10.1109/IIHMSP.2010.157
IIH-MSP
Keywords
Field
DocType
frequency-tuned nighttime brake-light detection,road vehicles,road safety,forward collision avoidance,brake lights detection,advanced safety vehicle,critical issue,traffic engineering computing,brake light detection,brake light,feature extraction,detection process,consequent event,cameras,intelligent system,proposed system,frequency domain,heuristic feature,brakes,collision avoidance,visual-based event detection,frequency domain analysis,shape,noise
Frequency domain,Computer vision,Brake,Heuristic,Computer science,Feature extraction,Collision,Invariant (mathematics),Artificial intelligence,Traffic conditions
Conference
ISBN
Citations 
PageRank 
978-0-7695-4222-5
6
0.67
References 
Authors
3
2
Name
Order
Citations
PageRank
Duan-Yu Chen129628.79
Yu-Hao Lin2202.46