원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.9 No.6
2014.06
pp.105-118
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this paper, a two-step approach for vehicles detection is proposed. The first step of approach is to approximate vehicles’ potential locations through searching for shadow area of vehicle low-part. In order to find these shadows, Haar-like feature with Adaboost was used to train a Haar detector offline and the relearning process with hard training samples is applied to increase detection rate. Based on the previous processing, ROI (Region of interest) + HOG + SVM algorithm is used for vehicle verification. At last, K-means approach is used to combine the similar detection results. The experimental results proved that our system could be used for real-time preceding vehicle detection robustly and accurately.
목차
Abstract
1. Introduction
2. Related Works
3. Shadow Detection Based on Haar Feature
4. Vehicle Verification Using HOG and SVM Algorithm
4.1. Vehicle Verification in Region of Interest (ROI)
4.2. Vehicle Features Extraction based on HOG Algorithm
4.3. Learning Edge Direction Based on SVM
5. Experimental Results
6. Conclusion
Acknowledgements
References
1. Introduction
2. Related Works
3. Shadow Detection Based on Haar Feature
4. Vehicle Verification Using HOG and SVM Algorithm
4.1. Vehicle Verification in Region of Interest (ROI)
4.2. Vehicle Features Extraction based on HOG Algorithm
4.3. Learning Edge Direction Based on SVM
5. Experimental Results
6. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보