Earticle

초록

영어

There are several methods proposed for detection and segmentation of object effectively. However, this algorithm struggles to detect an object with a lot of noise and shadows. Therefore, it is difficult to segment the accurate region and information of the object using background modeling only. To solve these problems, this paper introduced a more effective method of object segmentation based on interest point detection and description, which are core SURF theories. As a result, the feature extracted from the region of interest (ROI) was detecTable even with changes in scale, noise, and illumination. We then made the adaptive search window by this feature for ROI. After object detection, we applied the SVM to train the information of the feature from the detected object, and a classifier was built to estimate whether a result was a pedestrian. Therefore, if the result is a pedestrian, we would employ the Camshift algorithm to track the motion of this pedestrian. The experimental results showed the effectiveness of our method through comparison with others.

목차

Abstract
 1. Introduction
 2. Feature Selection for ROI
  2.1. ROI Detection of Pedestrian
  2.2. Feature Description for Pedestrian
 3. Pedestrian Detection and Tracking
  3.1. Pedestrian Detection
  3.2. Pedestrian Tracking
 4. Conclusion
 Acknowledgments
 References

키워드

저자

• Ming-Shou An [ Dong-A University, Dept. of Electronics Engineering, 37 Nakdong-daero 550 beon-gil Saha-gu, Busan, Korea ]
• Dae-Seong Kang [ Dong-A University, Dept. of Electronics Engineering, 37 Nakdong-daero 550 beon-gil Saha-gu, Busan, Korea ]

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