The Background Subtraction Problem for Video Surveillance Systems

McIvor, Alan M.; Zang, Qi; Klette, Reinhard

doi:10.1007/3-540-44690-7_22

Cited by 36 publications

(26 citation statements)

References 6 publications

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“…Cells in circulating blood are in constant motion. A popular approach for locating moving objects is background subtraction and temporal differencing of consecutive frames [31], [32]. Yet, in-vivo video sequences contain many moving elements besides circulating cells: vessel boundaries and tissue elements that shift as a result of respiratory movements and fluids that flow outside the vessels as well as continually circulating erythrocytes within the vessel.…”

Section: A Cell Detectionmentioning

confidence: 99%

An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy

Eden

Waisman²,

Rudzsky³

et al. 2005

IEEE Trans. Med. Imaging

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The behavior of white and red blood cells, platelets and circulating injected particles is one of the main focuses in physiological studies. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for a fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics.The task of automated blood cell tracking in vessels from in-vivo video is particularly challenging because of the blood cells' non-rigid shapes, the instability inherent in in-vivo videos, the abundance of moving objects and their frequent superposition.To tackle this, the CellTrack system operates on two levels: first, a global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel. Second, a local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles. The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in-vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information. A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of blood flow characteristics from in-vivo videos.

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Section: A Cell Detectionmentioning

confidence: 99%

An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy

Eden

Waisman²,

Rudzsky³

et al. 2005

IEEE Trans. Med. Imaging

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“…Background adaptation methods vary from monochromatic filtering [11], [3] and using various color spaces [14], [15], [16] to statistical background models [14], [17], [18]. A review of background subtraction in video surveillance systems can be found in [19]. In Section 2.1 we describe the background initialization process.…”

Section: Target Detectionmentioning

confidence: 99%

Classification of Moving Targets Based on Motion and Appearance

Bogomolov¹,

Dror²,

Lapchev³

et al. 2003

Procedings of the British Machine Vision Conference 2003

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We describe a system for detection and classification of moving targets. The system's change detection and tracking modules are based on background adaptation, with the help of information about targets obtained from preceding time steps. The classification module performs a hybrid classification that combines motion and appearance features. The system is able to perform real-time detection, tracking and classification of targets in outdoor settings. Experiments demonstrate that the proposed hybrid classifier architecture improves classification significantly, thereby permitting real-time discrimination among a considerable number of classes, some of which are quite similar.

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“…The paper is structured as follows: in Section 2, we discuss recent approaches for modeling a background using Gauss distributions, following [16]. Section 3 presents performance results of the chosen model for implementation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an Adaptive Composite Gaussian Model in Video Surveillance

Zang

Klette

2003

Computer Analysis of Images and Patterns

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Video surveillance systems seek to automatically identify events of interest in a variety of situations. Extracting a moving object from background is the most important step of the whole system. There are many approaches to track moving objects in a video surveillance system. These can be classified into three main groups: feature-based tracking, background subtraction, and optical flow techniques. Background subtraction is a region-based approach where the objective is to identify parts of the image plane that are significantly different to the background. In order to avoid the most common problems introduced by gradual illumination changes, waving trees, shadows, etc., the background scene requires a composite model. A mixture of Gaussian distributions is most popular. In this paper, we classify and discuss several recently proposed composite models. We have chosen one of these for implementation and evaluate its performance. We also analyzed its benefits and drawbacks, and designed an improved version of this model based on our experimental evaluation. One stationary camera has been used.Keywords: video sequence analysis, surveillance systems, Gaussian mixture models. You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the CITR Tamaki web site under terms that include this permission. All other rights are reserved by the author(s). Evaluation of an Adaptive Composite Gaussian Model in Video Surveillance

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The Background Subtraction Problem for Video Surveillance Systems

Cited by 36 publications

References 6 publications

An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy

An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy

Classification of Moving Targets Based on Motion and Appearance

Evaluation of an Adaptive Composite Gaussian Model in Video Surveillance

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