Change Detection and Object Tracking in IR Surveillance Video

Celenk, Mehmet; Venable, Don; Smearcheck, M.; Graham, James

doi:10.1109/sitis.2007.34

Cited by 2 publications

(2 citation statements)

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“…The objective of this study is to evaluate and compare different motion detection methods and identify the best method for different situations using a challenging complete dataset. To this end, we tested the following methods: temporal differencing (frame difference) 86,149,150 , threeframe difference (3FD) [151][152][153] , adaptive background (average filter) 90,154,155 , forgetting morphological temporal gradient (FMTG) 156 ,  Background estimation 157,158 , spatio-temporal Markov field [159][160][161] , running Gaussian average (RGA) 14,162,163 , mixture of Gaussians (MoG) 15,59,164 , spatio-temporal entropy image (STEI) 165,166 , difference-based spatio-temporal entropy image (DSTEI) 166,167 , eigen-background (Eig-Bg) 42,168,169 and simplified self-organized map (Simp-SOBS) 24 methods. Many of these methods (3FD,  ,FMTG, STEI, DSTEI, Simp-SOBS) have not been previously evaluated on challenging dataset, to this end, we used the CDnet2012 The remainder of this paper is organized as follows.…”

Section: Comparative Studiesmentioning

confidence: 99%

Comparative study of motion detection methods for video surveillance systems

Sehairi

Chouireb

Meunier

2017

J. Electron. Imaging

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The objective of this study is to compare several change detection methods for a mono static camera and identify the best method for different complex environments and backgrounds in indoor and outdoor scenes. To this end, we used the CDnet video dataset * as a benchmark that consists of many challenging problems, ranging from basic simple scenes to complex scenes affected by bad weather and dynamic backgrounds. Twelve change detection methods, ranging from simple temporal differencing to more sophisticated methods, were tested and several performance metrics were used to precisely evaluate the results. Because most of the considered methods have not previously been evaluated on this recent large scale dataset, this work compares these methods to fill a lack in the literature, and thus this evaluation joins as complementary compared with the previous comparative evaluations. Our experimental results show that there is no perfect method for all challenging cases; each method performs well in certain cases and fails in others. However, this study enables the user to identify the most suitable method for his or her needs.

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Section: Comparative Studiesmentioning

confidence: 99%

Comparative study of motion detection methods for video surveillance systems

Sehairi

Chouireb

Meunier

2017

J. Electron. Imaging

View full text Add to dashboard Cite

show abstract

“…There are various ways to realize the tracking applications according to the constraints of the problem. The common way for the stationary systems is to detect changes on a static background and extract foreground objects to be tracked (Celenk et al, 2008). In most cases, background modeling with change detection is not suitable for dynamic systems in which the observer is also moving.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Weighted Template Matching Using Invariant Features for a Tracking Application

İmamoğlu

Eresen

Efe

2010

IFAC Proceedings Volumes

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One of the common research topics for surveillance systems is tracking of a target object. Motions of both observer and target make the problem difficult to handle. In this paper, proposed algorithm tracks a target object on consecutive frames taken from a camera embedded on an unmanned aerial vehicle. Adaptive-fuzzy weighted sum of square distances (SSDs) are utilized for template matching process. SSD is computed by the pseudo-Zernike moments of the reference template and possible matching template around the search point. Fuzzy weights are obtained by taking the advantage of circular projection features. An adaptive fuzzy system is realized by updating the weights of the defuzzifier. Proposed method provides center point of the best matching template which represents the target object to be tracked. Tracking is accomplished under the presence of illumination changes and parallax, and it is seen that the proposed algorithm yields satisfactorily small errors.

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Change Detection and Object Tracking in IR Surveillance Video

Cited by 2 publications

References 10 publications

Comparative study of motion detection methods for video surveillance systems

Comparative study of motion detection methods for video surveillance systems

Adaptive Fuzzy Weighted Template Matching Using Invariant Features for a Tracking Application

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