Moving Target Detection and Tracking Using Edge Features Detection and Matching

Alireza BEHRAD; Seyed AHMAD MOTAMEDI

Moving Target Detection and Tracking Using Edge Features Detection and Matching

Alireza BEHRAD, Seyed AHMAD MOTAMEDI

Full Text Views

0

Cite this

Summary :

A new algorithm for fast detection and tracking of moving targets using a mobile video camera is presented. Our algorithm is based on image feature detection and matching. To detect features, we used edge points and their accumulated curvature. When the features are detected they are matched with their corresponding points using a new method called fuzzy-edge based feature matching. The proposed algorithm has two modes: detection and tracking. In the detection mode, background motion is estimated and compensated using an affine transformation. The resultant motion-rectified image is used for detection of the target location using split and merge algorithm. We also checked other features for precise detection of the target. When the target is identified, algorithm switches to the tracking mode, which also has two phases. In the first phase, the algorithm tracks the target with the intention to recover the target bounding-box more precisely and when the target bounding-box is determined precisely, the second phase of tracking algorithm starts to track the specified target more accurately. The algorithm has good performance in the environment with noise and illumination change.

Publication: IEICE TRANSACTIONS on Information Vol.E86-D No.12 pp.2764-2774

Publication Date: 2003/12/01

Publicized

Online ISSN

DOI

Type of Manuscript: PAPER

Category: Pattern Recognition

Cite this

Copy

Alireza BEHRAD, Seyed AHMAD MOTAMEDI, "Moving Target Detection and Tracking Using Edge Features Detection and Matching" in IEICE TRANSACTIONS on Information, vol. E86-D, no. 12, pp. 2764-2774, December 2003, doi: .
Abstract: A new algorithm for fast detection and tracking of moving targets using a mobile video camera is presented. Our algorithm is based on image feature detection and matching. To detect features, we used edge points and their accumulated curvature. When the features are detected they are matched with their corresponding points using a new method called fuzzy-edge based feature matching. The proposed algorithm has two modes: detection and tracking. In the detection mode, background motion is estimated and compensated using an affine transformation. The resultant motion-rectified image is used for detection of the target location using split and merge algorithm. We also checked other features for precise detection of the target. When the target is identified, algorithm switches to the tracking mode, which also has two phases. In the first phase, the algorithm tracks the target with the intention to recover the target bounding-box more precisely and when the target bounding-box is determined precisely, the second phase of tracking algorithm starts to track the specified target more accurately. The algorithm has good performance in the environment with noise and illumination change.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_12_2764/_p

Copy

@ARTICLE{e86-d_12_2764,
author={Alireza BEHRAD, Seyed AHMAD MOTAMEDI, },
journal={IEICE TRANSACTIONS on Information},
title={Moving Target Detection and Tracking Using Edge Features Detection and Matching},
year={2003},
volume={E86-D},
number={12},
pages={2764-2774},
abstract={A new algorithm for fast detection and tracking of moving targets using a mobile video camera is presented. Our algorithm is based on image feature detection and matching. To detect features, we used edge points and their accumulated curvature. When the features are detected they are matched with their corresponding points using a new method called fuzzy-edge based feature matching. The proposed algorithm has two modes: detection and tracking. In the detection mode, background motion is estimated and compensated using an affine transformation. The resultant motion-rectified image is used for detection of the target location using split and merge algorithm. We also checked other features for precise detection of the target. When the target is identified, algorithm switches to the tracking mode, which also has two phases. In the first phase, the algorithm tracks the target with the intention to recover the target bounding-box more precisely and when the target bounding-box is determined precisely, the second phase of tracking algorithm starts to track the specified target more accurately. The algorithm has good performance in the environment with noise and illumination change.},
keywords={},
doi={},
ISSN={},
month={December},}

Copy

TY - JOUR
TI - Moving Target Detection and Tracking Using Edge Features Detection and Matching
T2 - IEICE TRANSACTIONS on Information
SP - 2764
EP - 2774
AU - Alireza BEHRAD
AU - Seyed AHMAD MOTAMEDI
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2003
AB - A new algorithm for fast detection and tracking of moving targets using a mobile video camera is presented. Our algorithm is based on image feature detection and matching. To detect features, we used edge points and their accumulated curvature. When the features are detected they are matched with their corresponding points using a new method called fuzzy-edge based feature matching. The proposed algorithm has two modes: detection and tracking. In the detection mode, background motion is estimated and compensated using an affine transformation. The resultant motion-rectified image is used for detection of the target location using split and merge algorithm. We also checked other features for precise detection of the target. When the target is identified, algorithm switches to the tracking mode, which also has two phases. In the first phase, the algorithm tracks the target with the intention to recover the target bounding-box more precisely and when the target bounding-box is determined precisely, the second phase of tracking algorithm starts to track the specified target more accurately. The algorithm has good performance in the environment with noise and illumination change.
ER -