Earticle

초록

영어

This paper proposes an unsupervised learning framework in which models of objects’ appearance classes are learned using their spatio and temporal information, from video. These models are used to detect objects of different classes in the everyday scene. The proposed technique combines appearance and motion features in a weighted combination framework resulting in models of object classes. Thus, better detection results are achieved compared to foreground based tracking and to those obtained in a supervised way. Since the proposed technique is unsupervised, a good detection rate is achieved without manual effort expended in data collection and labelling. Experimental results confirm that the proposed framework offers a promising solution for detection in unfamiliar scenes.

목차

Abstract
 1. Introduction
 2. Unsupervised Acquisition of Training Data
  2.1. Feature Extraction
  2.2. Combining Features
  2.3. Clustering to obtain classes
  2.4. Parameter Estimation
 3. Training and Using Multiple Detectors
  3.1. Training Phase
  3.2. Object Representation
  3.3. Detection Phase
 4. Experiments and Evaluation
  4.1. Evaluation of Bank of Detectors
 5. Conclusion
 References

키워드

저자

• Najeed Ahmed Khan [ NED University of Engineering and Technology Karachi, Pakistan ]
• David C. Hogg [ School of Computing University of Leeds, UK ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605