A novel approach for people counting and tracking from crowd video

M. Ayyuce Kizrak Sagun; Bulent Bolat

doi:10.1109/INISTA.2017.8001170

A novel approach for people counting and tracking from crowd video

M. Ayyuce Kizrak Sagun, Bulent Bolat

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

6 Citations (Scopus)

Abstract

Crowd analysis on video recordings is an important research area currently. In this work, a combined crowd density estimation method is presented to overcome this problem. To improve the accuracy of the system two different estimators run simultaneously and a blob is marked as a person only if both estimators mark it as person. One of the main problems in crowd density estimation is occlusion. To overcome this problem we tracked the trajectories of blobs by using a Kalman filter. The method was applied to three common benchmark data which are PETS2009, UCSD and Grand Central. The results confirm the proposed method's success.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017
Editors	Tulay Yildirim, Ireneusz Czarnowski, Piotr Jedrzejowicz
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	277-281
Number of pages	5
ISBN (Electronic)	9781509057955
DOIs	https://doi.org/10.1109/INISTA.2017.8001170
Publication status	Published - 3 Aug 2017
Externally published	Yes
Event	2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017 - Gdynia, Poland Duration: 3 Jul 2017 → 5 Jul 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017

Conference

Conference	2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017
Country/Territory	Poland
City	Gdynia
Period	3/07/17 → 5/07/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

complex wavelet transform
Crowd density
Kalman filtering
Optical flow
SIFT
Video processing

Access to Document

10.1109/INISTA.2017.8001170

Cite this

Sagun, M. A. K., & Bolat, B. (2017). A novel approach for people counting and tracking from crowd video. In T. Yildirim, I. Czarnowski, & P. Jedrzejowicz (Eds.), Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017 (pp. 277-281). Article 8001170 (Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INISTA.2017.8001170

Sagun, M. Ayyuce Kizrak ; Bolat, Bulent. / A novel approach for people counting and tracking from crowd video. Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017. editor / Tulay Yildirim ; Ireneusz Czarnowski ; Piotr Jedrzejowicz. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 277-281 (Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017).

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abstract = "Crowd analysis on video recordings is an important research area currently. In this work, a combined crowd density estimation method is presented to overcome this problem. To improve the accuracy of the system two different estimators run simultaneously and a blob is marked as a person only if both estimators mark it as person. One of the main problems in crowd density estimation is occlusion. To overcome this problem we tracked the trajectories of blobs by using a Kalman filter. The method was applied to three common benchmark data which are PETS2009, UCSD and Grand Central. The results confirm the proposed method's success.",

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year = "2017",

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Sagun, MAK & Bolat, B 2017, A novel approach for people counting and tracking from crowd video. in T Yildirim, I Czarnowski & P Jedrzejowicz (eds), Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017., 8001170, Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017, Institute of Electrical and Electronics Engineers Inc., pp. 277-281, 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017, Gdynia, Poland, 3/07/17. https://doi.org/10.1109/INISTA.2017.8001170

A novel approach for people counting and tracking from crowd video. / Sagun, M. Ayyuce Kizrak; Bolat, Bulent.
Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017. ed. / Tulay Yildirim; Ireneusz Czarnowski; Piotr Jedrzejowicz. Institute of Electrical and Electronics Engineers Inc., 2017. p. 277-281 8001170 (Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017).

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

TY - GEN

T1 - A novel approach for people counting and tracking from crowd video

AU - Sagun, M. Ayyuce Kizrak

AU - Bolat, Bulent

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N2 - Crowd analysis on video recordings is an important research area currently. In this work, a combined crowd density estimation method is presented to overcome this problem. To improve the accuracy of the system two different estimators run simultaneously and a blob is marked as a person only if both estimators mark it as person. One of the main problems in crowd density estimation is occlusion. To overcome this problem we tracked the trajectories of blobs by using a Kalman filter. The method was applied to three common benchmark data which are PETS2009, UCSD and Grand Central. The results confirm the proposed method's success.

AB - Crowd analysis on video recordings is an important research area currently. In this work, a combined crowd density estimation method is presented to overcome this problem. To improve the accuracy of the system two different estimators run simultaneously and a blob is marked as a person only if both estimators mark it as person. One of the main problems in crowd density estimation is occlusion. To overcome this problem we tracked the trajectories of blobs by using a Kalman filter. The method was applied to three common benchmark data which are PETS2009, UCSD and Grand Central. The results confirm the proposed method's success.

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Sagun MAK, Bolat B. A novel approach for people counting and tracking from crowd video. In Yildirim T, Czarnowski I, Jedrzejowicz P, editors, Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 277-281. 8001170. (Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017). doi: 10.1109/INISTA.2017.8001170

A novel approach for people counting and tracking from crowd video

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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