Earticle

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

키워드

저자

• Huan Wang [ School of Information Science and Engineering, Dalian Polytechnic University, 116034 Dalian, China ]
• Haichuan Zhang [ School of Information Science and Engineering, Dalian Polytechnic University, 116034 Dalian, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605