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Mathematical Problems in Engineering
Volume 2014, Article ID 513283, 20 pages
http://dx.doi.org/10.1155/2014/513283
Research Article

Invariant Hough Random Ferns for Object Detection and Tracking

1Institute of Optical Communication & Optoelectronics, Beijing University of Posts & Telecommunications (BUPT), No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
2School of Instrumentation Science & Optoelectronics Engineering, Beijing Information Science & Technology University (BISTU), No. 12 Qinghe Xiaoying East Road, Haidian District, Beijing 100192, China
3Beijing Aeronautical Manufacturing Technology Research Institute, Beijing 100024, China

Received 8 December 2013; Revised 6 February 2014; Accepted 13 February 2014; Published 8 April 2014

Academic Editor: Ilse C. Cervantes

Copyright © 2014 Yimin Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper introduces an invariant Hough random ferns (IHRF) incorporating rotation and scale invariance into the local feature description, random ferns classifier training, and Hough voting stages. It is especially suited for object detection under changes in object appearance and scale, partial occlusions, and pose variations. The efficacy of this approach is validated through experiments on a large set of challenging benchmark datasets, and the results demonstrate that the proposed method outperforms state-of-the-art conventional methods such as bounding-box-based and part-based methods. Additionally, we also propose an efficient clustering scheme based on the local patches’ appearance and their geometric relations that can provide pixel-accurate, top-down segmentations from IHRF back-projections. This refined segmentation can be used to improve the quality of online object tracking because it avoids the drifting problem. Thus, an online tracking framework based on IHRF, which is trained and updated in each frame to distinguish and segment the object from the background, is established. Finally, the experimental results on both object segmentation and long-term object tracking show that this method yields accurate and robust tracking performance in a variety of complex scenarios, especially in cases of severe occlusions and nonrigid deformations.