통계적 특징 기반 SVM을 이용한 야간 전방 차량 검출 기법
- 통계적 특징 기반 SVM을 이용한 야간 전방 차량 검출 기법
- Other Titles
- Night Time Leading Vehicle Detection Using Statistical Feature Based SVM
- 정호열; 정정은[정정은]; 김현구; 박주현
- Night time vehicle detection; SVM feature extraction; Color space; Image processing
- Issue Date
- 대한임베디드공학회논문지, v.7, no.4, pp.163 - 172
- A driver assistance system is critical to improve a convenience and stability of vehicle driving. Several systems have been already commercialized such as adaptive cruise control system and forward collision warning system. Efficient vehicle detection is very important to improve such driver assistance systems. Most existing vehicle detection systems are based on a radar system, which measures distance between a host and leading (or oncoming) vehicles under various weather conditions. However, it requires high deployment cost and complexity overload when there are many vehicles. A camera based vehicle detection technique is also good alternative method because of low cost and simple implementation. In general, night time vehicle detection is more complicated than day time vehicle detection, because it is much more difficult to distinguish the vehicle’s features such as outline and color under the dim environment. This paper proposes a method to detect vehicles at night time using analysis of a captured color space with reduction of reflection and other light sources in images. Four colors spaces, namely RGB, YCbCr, normalized RGB and Ruta-RGB, are compared each other and evaluated. A suboptimal threshold value is determined by Otsu algorithm and applied to extract candidates of taillights of leading vehicles. Statistical features such as mean, variance, skewness, kurtosis, and entropy are extracted from the candidate regions and used as feature vector for SVM(Support Vector Machine) classifier. According to our simulation results, the proposed statistical feature based SVM provides relatively high performances of leading vehicle detection with various distances in variable nighttime environments.
- Appears in Collections:
- 공과대학 > 모바일정보통신공학과 > Articles
공과대학 > 전기공학과 > Articles
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)