IEICE TRANSACTIONS on Information
Entropy Based Illumination-Invariant Foreground Detection
Summary :
Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.7 pp.1434-1437
- Publication Date
- 2019/07/01
- Publicized
- 2019/04/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8247
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Karthikeyan PANJAPPAGOUNDER RAJAMANICKAM
Anna University
Sakthivel PERIYASAMY
Anna University
Keyword
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Karthikeyan PANJAPPAGOUNDER RAJAMANICKAM, Sakthivel PERIYASAMY, "Entropy Based Illumination-Invariant Foreground Detection" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1434-1437, July 2019, doi: 10.1587/transinf.2018EDL8247.
Abstract: Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8247/_p
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@ARTICLE{e102-d_7_1434,
author={Karthikeyan PANJAPPAGOUNDER RAJAMANICKAM, Sakthivel PERIYASAMY, },
journal={IEICE TRANSACTIONS on Information},
title={Entropy Based Illumination-Invariant Foreground Detection},
year={2019},
volume={E102-D},
number={7},
pages={1434-1437},
abstract={Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.},
keywords={},
doi={10.1587/transinf.2018EDL8247},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Entropy Based Illumination-Invariant Foreground Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1434
EP - 1437
AU - Karthikeyan PANJAPPAGOUNDER RAJAMANICKAM
AU - Sakthivel PERIYASAMY
PY - 2019
DO - 10.1587/transinf.2018EDL8247
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.
ER -
English
