원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.5 No.4
2012.12
pp.83-92
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
A new method, 2DPCA-GMM of tracking and segmentation in the dynamic environment of objects is proposed in this paper. The method attempts to link the Gaussian mixture model, (GMM) with the method of two dimensional principal component analysis (2DPCA) and apply Kalman Filtering (KF) for tracking. In this context, the aim of the paper is to tackle tracking of moving object based on 2DPCA-GMM together with Kalman prediction of the position and size of object along the image’s sequence. The obtained results successfully illustrate the tracking of a single moving object as well as multiple moving objects based on segmentation generated by 2DPCA-GMM.
목차
Abstract
1. Introduction
2. Adaptive Background Subtraction Algorithm
3. Two Dimensional Principal Component Analysis (2DPCA)
4. The 2DPCA-GMM Integrated Algorithm
5. Kalman Filtering
6. Experiment Results and Analysis
7. Conclusion
References
1. Introduction
2. Adaptive Background Subtraction Algorithm
3. Two Dimensional Principal Component Analysis (2DPCA)
4. The 2DPCA-GMM Integrated Algorithm
5. Kalman Filtering
6. Experiment Results and Analysis
7. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보