Visual object tracking using improved Mean Shift algorithm

Sulfan Bagus Setyawan; Djoko Purwanto; Ronny Mardiyanto

doi:10.1109/ICITSI.2015.7437677

Visual object tracking using improved Mean Shift algorithm

Sulfan Bagus Setyawan
, Djoko Purwanto
, Ronny Mardiyanto

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram is added in target model to reduce background interference. Then, Scale adaptive methods is added in Mean Shift for scaling. Occlusion detection is handled by scaled Normalized Cross Correlation. The results prove that the proposed method is robust from noise background, scaling and occlusion detection.

Original language	English
Title of host publication	2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467366649
DOIs	https://doi.org/10.1109/ICITSI.2015.7437677
Publication status	Published - 21 Mar 2016
Event	2nd International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Bandung, Bali, Indonesia Duration: 16 Nov 2015 → 19 Nov 2015

Publication series

Name	2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings

Conference

Conference	2nd International Conference on Information Technology Systems and Innovation, ICITSI 2015
Country/Territory	Indonesia
City	Bandung, Bali
Period	16/11/15 → 19/11/15

Keywords

Corrected Background Weighted Histogram
Mean Shift
Normalized Cross Correlation
Scaling
Visual Object tracking

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10.1109/ICITSI.2015.7437677

Cite this

Setyawan, S. B., Purwanto, D., & Mardiyanto, R. (2016). Visual object tracking using improved Mean Shift algorithm. In 2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings Article 7437677 (2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITSI.2015.7437677

Setyawan, Sulfan Bagus ; Purwanto, Djoko ; Mardiyanto, Ronny. / Visual object tracking using improved Mean Shift algorithm. 2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. (2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings).

@inproceedings{bc41297fec2c4d1b9aaace07a1a5ba4d,

title = "Visual object tracking using improved Mean Shift algorithm",

abstract = "Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram is added in target model to reduce background interference. Then, Scale adaptive methods is added in Mean Shift for scaling. Occlusion detection is handled by scaled Normalized Cross Correlation. The results prove that the proposed method is robust from noise background, scaling and occlusion detection.",

keywords = "Corrected Background Weighted Histogram, Mean Shift, Normalized Cross Correlation, Scaling, Visual Object tracking",

author = "Setyawan, \{Sulfan Bagus\} and Djoko Purwanto and Ronny Mardiyanto",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2nd International Conference on Information Technology Systems and Innovation, ICITSI 2015 ; Conference date: 16-11-2015 Through 19-11-2015",

year = "2016",

month = mar,

day = "21",

doi = "10.1109/ICITSI.2015.7437677",

language = "English",

series = "2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings",

address = "United States",

}

Setyawan, SB, Purwanto, D & Mardiyanto, R 2016, Visual object tracking using improved Mean Shift algorithm. in 2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings., 7437677, 2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Information Technology Systems and Innovation, ICITSI 2015, Bandung, Bali, Indonesia, 16/11/15. https://doi.org/10.1109/ICITSI.2015.7437677

Visual object tracking using improved Mean Shift algorithm. / Setyawan, Sulfan Bagus; Purwanto, Djoko ; Mardiyanto, Ronny.
2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7437677 (2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Visual object tracking using improved Mean Shift algorithm

AU - Setyawan, Sulfan Bagus

AU - Purwanto, Djoko

AU - Mardiyanto, Ronny

PY - 2016/3/21

Y1 - 2016/3/21

N2 - Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram is added in target model to reduce background interference. Then, Scale adaptive methods is added in Mean Shift for scaling. Occlusion detection is handled by scaled Normalized Cross Correlation. The results prove that the proposed method is robust from noise background, scaling and occlusion detection.

AB - Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram is added in target model to reduce background interference. Then, Scale adaptive methods is added in Mean Shift for scaling. Occlusion detection is handled by scaled Normalized Cross Correlation. The results prove that the proposed method is robust from noise background, scaling and occlusion detection.

KW - Corrected Background Weighted Histogram

KW - Mean Shift

KW - Normalized Cross Correlation

KW - Scaling

KW - Visual Object tracking

UR - https://www.scopus.com/pages/publications/84966938189

U2 - 10.1109/ICITSI.2015.7437677

DO - 10.1109/ICITSI.2015.7437677

M3 - Conference contribution

AN - SCOPUS:84966938189

T3 - 2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings

BT - 2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Information Technology Systems and Innovation, ICITSI 2015

Y2 - 16 November 2015 through 19 November 2015

ER -

Setyawan SB, Purwanto D , Mardiyanto R. Visual object tracking using improved Mean Shift algorithm. In 2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 7437677. (2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings). doi: 10.1109/ICITSI.2015.7437677

Visual object tracking using improved Mean Shift algorithm

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