Earticle

초록

영어

Sliding window (SW) technique is one of common paradigms employed for object detection. However, the computational cost of this approach is so expensive because the detection window is scanned at all possible positions and scales. To overcome this problem, we propose a compact feature together with fast recursive coarse-to-fine object localization strategy. To build a compact feature, we project the Histograms of Oriented Gradient (HOG) features to linear subspace by Principal Component Analysis (PCA). We call this feature as PCA-HOG feature. The exploitation of the PCA-HOG feature not only helps the classifiers run faster but also still maintains the accuracy. In order to further speeding up the localization, we propose a recursive coarse-to-fine refinement to scan image. We scan image in both scale space and multi-resolution space from coarsest to finest resolutions. Only the best obtained hypothesis from the coarser resolution could be passed to finer resolution. Each resolution has its own linear Support Vector Machine (SVM) classifier and PCA-HOG features. Evaluation with INRIA dataset shows that our method achieves a significant speed-up compared to standard sliding window and original HOG feature, while even get higher detection accuracy.

목차

Abstract
 1. Introduction
 2. Proposed Methods
  2.1. Features Extraction
  2.2. Recursive Coarse-to-Fine Scanning Scheme
 3. Experimential Results
  3.1. Feature Evaluation with Per-Window Methodology
  3.2. Feature Design for Resolution Levels
  3.3. Detecion Evaluation with Per-Image Methodology
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Quy Nguyen Trung [ School of Electronics and Computer Engineering Chonnam National University ]
• Dung Phan [ School of Electronics and Computer Engineering Chonnam National University ]
• Soo Hyung Kim [ School of Electronics and Computer Engineering Chonnam National University ]
• In Seop Na [ School of Electronics and Computer Engineering Chonnam National University ] Corresponding author
• Hyung Jeong Yang [ School of Electronics and Computer Engineering Chonnam National University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605