Abstract
This paper proposes a new visual tracking framework and demonstrates its merits via mobile robot experiments. An image sequence from the vision system of a mobile robot is not static when a mobile robot is moving, since slipping and vibration occur. These problems cause image blurring. Therefore, in this paper, we address the problem of robust object tracking under blurring and introduce a novel robust visual tracking framework based on the arbitration of the AdaBoost-based detection method and the appearance-based detection method to overcome the blurring problem. The proposed framework consists of three parts: (1) distortion error compensation and feature extraction using the Modified Discrete Gaussian–Hermite Moment (MDGHM) and fuzzy-based distortion error compensation, (2) object detection using arbitration of appearance- and feature-based object detection, and (3) object tracking using a Finite Impulse Response (FIR) filter. To demonstrate the performance of the proposed framework, mobile robot visual tracking experiments are carried out. The results show that the proposed framework is more robust against blurring than the conventional feature- and appearance-based methods.
| Original language | English |
|---|---|
| Pages (from-to) | 50-69 |
| Number of pages | 20 |
| Journal | Measurement: Journal of the International Measurement Confederation |
| Volume | 95 |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
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Keywords
- Finite impulse response tracker
- Mobile robot
- Modified discrete Gaussian–Hermite moment
- Object detection
- Visual object tracking
ASJC Scopus subject areas
- Statistics and Probability
- Education
- Condensed Matter Physics
- Applied Mathematics
Cite this
Robust visual tracking framework in the presence of blurring by arbitrating appearance- and feature-based detection. / Kang, Tae Koo; Mo, Yung Hak; Pae, Dong Sung; Ahn, Choon Ki; Lim, Myo Taeg.
In: Measurement: Journal of the International Measurement Confederation, Vol. 95, 01.01.2017, p. 50-69.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Robust visual tracking framework in the presence of blurring by arbitrating appearance- and feature-based detection
AU - Kang, Tae Koo
AU - Mo, Yung Hak
AU - Pae, Dong Sung
AU - Ahn, Choon Ki
AU - Lim, Myo Taeg
PY - 2017/1/1
Y1 - 2017/1/1
N2 - This paper proposes a new visual tracking framework and demonstrates its merits via mobile robot experiments. An image sequence from the vision system of a mobile robot is not static when a mobile robot is moving, since slipping and vibration occur. These problems cause image blurring. Therefore, in this paper, we address the problem of robust object tracking under blurring and introduce a novel robust visual tracking framework based on the arbitration of the AdaBoost-based detection method and the appearance-based detection method to overcome the blurring problem. The proposed framework consists of three parts: (1) distortion error compensation and feature extraction using the Modified Discrete Gaussian–Hermite Moment (MDGHM) and fuzzy-based distortion error compensation, (2) object detection using arbitration of appearance- and feature-based object detection, and (3) object tracking using a Finite Impulse Response (FIR) filter. To demonstrate the performance of the proposed framework, mobile robot visual tracking experiments are carried out. The results show that the proposed framework is more robust against blurring than the conventional feature- and appearance-based methods.
AB - This paper proposes a new visual tracking framework and demonstrates its merits via mobile robot experiments. An image sequence from the vision system of a mobile robot is not static when a mobile robot is moving, since slipping and vibration occur. These problems cause image blurring. Therefore, in this paper, we address the problem of robust object tracking under blurring and introduce a novel robust visual tracking framework based on the arbitration of the AdaBoost-based detection method and the appearance-based detection method to overcome the blurring problem. The proposed framework consists of three parts: (1) distortion error compensation and feature extraction using the Modified Discrete Gaussian–Hermite Moment (MDGHM) and fuzzy-based distortion error compensation, (2) object detection using arbitration of appearance- and feature-based object detection, and (3) object tracking using a Finite Impulse Response (FIR) filter. To demonstrate the performance of the proposed framework, mobile robot visual tracking experiments are carried out. The results show that the proposed framework is more robust against blurring than the conventional feature- and appearance-based methods.
KW - Finite impulse response tracker
KW - Mobile robot
KW - Modified discrete Gaussian–Hermite moment
KW - Object detection
KW - Visual object tracking
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U2 - 10.1016/j.measurement.2016.09.032
DO - 10.1016/j.measurement.2016.09.032
M3 - Article
VL - 95
SP - 50
EP - 69
JO - Measurement
JF - Measurement
SN - 1536-6367
ER -