Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework

Dejan Arsić; Atanas Lyutskanov; Gerhard Rigoll; Bogdan Kwolek

doi:10.1109/PETS-WINTER.2009.5399723

Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework

Dejan Arsić, Atanas Lyutskanov, Gerhard Rigoll, Bogdan Kwolek

Chair of Human-Machine Communication

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

19 Scopus citations

Abstract

Reliable tracking of objects is an inevitable prerequisite for automated video surveillance systems. As most object detection methods, which are based on machine learning, require adequate data for the application scenario, foreground segmentation is a popular method to find possible regions of interest. These usually require a specific learning phase and adaptation over time. In this work we will present a novel approach based on graph cuts, which outperforms most standard algorithms. It is commonly agreed that occlusions can only be resolved in multi camera environments. Applying multi layer homography will enable us to robustly detect and track objects applying only foreground data, resulting in a high tracking performance.

Original language	English
Title of host publication	Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009
DOIs	https://doi.org/10.1109/PETS-WINTER.2009.5399723
State	Published - 2009
Event	12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009 - Snowbird, UT, United States Duration: 7 Dec 2009 → 9 Dec 2009

Publication series

Name	Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009

Conference

Conference	12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009
Country/Territory	United States
City	Snowbird, UT
Period	7/12/09 → 9/12/09

Access to Document

10.1109/PETS-WINTER.2009.5399723

Cite this

Arsić, D., Lyutskanov, A., Rigoll, G., & Kwolek, B. (2009). Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework. In Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009 Article 5399723 (Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009). https://doi.org/10.1109/PETS-WINTER.2009.5399723

Arsić, Dejan ; Lyutskanov, Atanas ; Rigoll, Gerhard et al. / Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework. Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009. 2009. (Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009).

@inproceedings{88d0726c8ef7462aabf71220288c65e0,

title = "Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework",

abstract = "Reliable tracking of objects is an inevitable prerequisite for automated video surveillance systems. As most object detection methods, which are based on machine learning, require adequate data for the application scenario, foreground segmentation is a popular method to find possible regions of interest. These usually require a specific learning phase and adaptation over time. In this work we will present a novel approach based on graph cuts, which outperforms most standard algorithms. It is commonly agreed that occlusions can only be resolved in multi camera environments. Applying multi layer homography will enable us to robustly detect and track objects applying only foreground data, resulting in a high tracking performance.",

author = "Dejan Arsi{\'c} and Atanas Lyutskanov and Gerhard Rigoll and Bogdan Kwolek",

year = "2009",

doi = "10.1109/PETS-WINTER.2009.5399723",

language = "English",

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booktitle = "Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009",

note = "12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009 ; Conference date: 07-12-2009 Through 09-12-2009",

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Arsić, D, Lyutskanov, A, Rigoll, G & Kwolek, B 2009, Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework. in Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009., 5399723, Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009, 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009, Snowbird, UT, United States, 7/12/09. https://doi.org/10.1109/PETS-WINTER.2009.5399723

Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework. / Arsić, Dejan; Lyutskanov, Atanas; Rigoll, Gerhard et al.
Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009. 2009. 5399723 (Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009).

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

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Arsić D, Lyutskanov A, Rigoll G, Kwolek B. Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework. In Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009. 2009. 5399723. (Proceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009). doi: 10.1109/PETS-WINTER.2009.5399723

Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework

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