SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos

Işık, Şahin; Özkan, Kemal; Günal, Serkan; Gerek, Ömer Nezih

doi:10.1117/1.jei.27.2.023002

Cited by 49 publications

(19 citation statements)

References 16 publications

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“…The light-yellow box was a static object on the floor which was then moved onto the sofa. A ghost was left on the floor (marked using red ◇) in the detection results of SuBSENSE [ 11 ] and SWCD [ 13 ]. However, our two-layer not only suppressed ghosts, but also detected camouflaged static foreground object (marked using green 〇 in Figure 2 ) because of the adaptive distance and LBSP threshold in Section 2.4 .…”

Section: Methodsmentioning

confidence: 99%

“…The distance threshold

is an extremely important parameter which adjusts the precision and sensitivity of the background model for the local changes. In initial time, the color threshold

is set to 30 in SuBSENSE [ 11 ], 23 in WeSamBE [ 12 ], and 35 in SWCD [ 13 ], and the texture threshold

is set to 3 for all the experimental scenarios. Then, they are automatically adjusted over time based on the historical detection results of each pixel.…”

Section: Methodsmentioning

confidence: 99%

“…Here,

, and

are user-defined parameters. Comprehensively considering the research results described by the authors of [ 11 , 12 , 13 ], we bound

and

to the intervals [10,40] and [ 1 , 6 ], respectively, to adapt most of the practical environments. Thus, we defined

, and

in the paper.…”

Section: Methodsmentioning

confidence: 99%

“…Background subtraction (BS) is one of the most widely used techniques in change detection, and its performance depends mainly on the background modeling methods. To date, the popular background modeling methods can be classified into the following four categories: GMM-based [ 6 , 7 , 8 , 9 ], sample-based [ 10 , 11 , 12 , 13 ], clustering-based [ 14 , 15 , 16 , 17 ], and artificial neural network-based [ 18 , 19 , 20 , 21 , 22 ]. Each model has its own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…Actually, it is difficult to obtain accurate edge in complex scenarios because of the shadow, noise, and camouflage. The literatures [ 11 , 12 , 13 , 16 , 23 ] accelerated the elimination of ghosts by the spatial diffusion mechanism because of high similarities between the ghost region and the surrounding background. However, the camouflaged object is incorporated into the background.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ghost Detection and Removal Based on Two-Layer Background Model and Histogram Similarity

Zhang

2020

Sensors

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Detecting and removing ghosts is an important challenge for moving object detection because ghosts will remain forever once formed, leading to the overall detection performance degradation. To deal with this issue, we first classified the ghosts into two categories according to the way they were formed. Then, the sample-based two-layer background model and histogram similarity of ghost areas were proposed to detect and remove the two types of ghosts, respectively. Furthermore, three important parameters in the two-layer model, i.e., the distance threshold, similarity threshold of local binary similarity pattern (LBSP), and time sub-sampling factor, were automatically determined by the spatial-temporal information of each pixel for adapting to the scene change rapidly. The experimental results on the CDnet 2014 dataset demonstrated that our proposed algorithm not only effectively eliminated ghost areas, but was also superior to the state-of-the-art approaches in terms of the overall performance.

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Section: Methodsmentioning

confidence: 99%

“…The distance threshold

is an extremely important parameter which adjusts the precision and sensitivity of the background model for the local changes. In initial time, the color threshold

is set to 30 in SuBSENSE [ 11 ], 23 in WeSamBE [ 12 ], and 35 in SWCD [ 13 ], and the texture threshold

is set to 3 for all the experimental scenarios. Then, they are automatically adjusted over time based on the historical detection results of each pixel.…”

Section: Methodsmentioning

confidence: 99%

“…Here,

, and

are user-defined parameters. Comprehensively considering the research results described by the authors of [ 11 , 12 , 13 ], we bound

and

to the intervals [10,40] and [ 1 , 6 ], respectively, to adapt most of the practical environments. Thus, we defined

, and

in the paper.…”

Section: Methodsmentioning

confidence: 99%