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Foreground segmentation with PTZ camera: a survey

A Correction to this article was published on 28 December 2021

This article has been updated


The alertness of terrorism in the present is greater than that in the past with reference to the incident of September 11. Still now, there has been a fight against terrorism and that has triggered a novel effort to locate the enhanced approaches with a higher-end camera. A Pan Tilt Zoom (PTZ) camera, which is a type of such high-end camera with multi-functionalities, can be used for identifying such potential threats. Consequently, the background modeling has an increasing significance in the computer vision to segment the foreground objects for further analysis in video surveillance applications. A PTZ camera offers a lot of benefits over normal fixed cameras. It provides an easy installation with 360° plane and greater flexibility. Although numerous surveys on static camera methods have already been proposed to model background, these methods do not adopt maximized large-scale scene coverage as well as frame quality to recognize specific targets compared to the PTZ camera. This motivates the survey to address the issues and techniques related to the PTZ background modeling, since there is no survey on this emerging area. The sole objective of this paper is to present a brief survey on the PTZ camera-based foreground segmentation method, which is very indispensable for high level analysis. It also provides an overview of various techniques from the literature that addresses the challenges, solutions, key aspects of the PTZ camera-based foreground segmentation methods, categorization of different approaches as well as the available datasets used for experimentation, and important future scope along with left over challenges for the computer vision researchers with applications.

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This work has been supported under the Department of Science and Technology (DST) Fast Track Young Scientist Scheme for the project entitled, “Intelligent Surveillance System for Crowd Density Estimation and Human Action Analysis” with reference no. SR/FTP/ETA-49/2012.

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The original online version of this article was revised: In the first line of Abstract, a character ‘b’ was incorrectly added and an “e” was missing in the word ‘fficient’ found in the first line of Inference/Future Work/Improvements (8th row of 13th column) of Table.5.

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Komagal, E., Yogameena, B. Foreground segmentation with PTZ camera: a survey. Multimed Tools Appl 77, 22489–22542 (2018).

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  • Foreground segmentation
  • PTZ camera
  • Multi-functionality
  • Coverage
  • Computer vision