Algorithm of moving object detection with illumination robustness based on confidence

Cao, Jianzhong; Zhao, Shusheng; Sun, Xuemei; Wang, Yameng

doi:10.1109/itnec.2017.8284901

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aiming at the phenomenon that the ViBe algorithm is prone to ghosting and sensitive to light changes, many researchers have proposed a series of improvements [6][7][8][9][10][11][12][13][14][15][16][17]. [6] proposed the use of TOM (Time of map) mechanism to suppress ghost images.…”

Section: Introductionmentioning

confidence: 99%

“…The improved color difference formula has low sensitivity to changes in illumination. [15] proposed a moving target detection method based on the fusion of color features and XCS-LBP (eXtended Center Symmetric-Local Binary Pattern) based on confidence. [16] proposed an adaptive ViBe algorithm based on dispersion coefficient and spatial consistency factor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved ViBe Algorithm of Moving Target Extraction for Night Infrared Surveillance Video

2021

KSII TIIS

View full text Add to dashboard Cite

For the research field of night infrared surveillance video, the target imaging in the video is easily affected by the light due to the characteristics of the active infrared camera and the classical ViBe algorithm has some problems for moving target extraction because of background misjudgment, noise interference, ghost shadow and so on. Therefore, an improved ViBe algorithm (I-ViBe) for moving target extraction in night infrared surveillance video is proposed in this paper. Firstly, the video frames are sampled and judged by the degree of light influence, and the video frame is divided into three situations: no light change, small light change, and severe light change. Secondly, the ViBe algorithm is extracted the moving target when there is no light change. The segmentation factor of the ViBe algorithm is adaptively changed to reduce the impact of the light on the ViBe algorithm when the light change is small. The moving target is extracted using the region growing algorithm improved by the image entropy in the differential image of the current frame and the background model when the illumination changes drastically. Based on the results of the simulation, the I-ViBe algorithm proposed has better robustness to the influence of illumination. When extracting moving targets at night the I-ViBe algorithm can make target extraction more accurate and provide more effective data for further night behavior recognition and target tracking.

show abstract