Invariant Hough Random Ferns for Object Detection and Tracking

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 o...

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Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (2014), p.1-20
Main Authors: Lin, Yimin, Lu, Naiguang, Lou, Xiaoping, Zou, Fang, Yao, Yanbin, Du, Zhaocai
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Conferences
Distance learning
Drift
Ferns
Hypotheses
Invariants
Laboratories
Methods
Noise
Object recognition
Occlusion
Online
Scale invariance
Segmentation
Tracking
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/513283