Moving object detection based on improved ViBe algorithm

2021 ◽  
Author(s):  
Kun Liu ◽  
Junping Zhang
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Vibe Algorithm

Moving Object Detection Based on Improved ViBe Algorithm

2015 ◽  
Vol 9 (12) ◽  
pp. 225-232 ◽  
Author(s):  
Gu Bo ◽  
Song Kefeng ◽  
Qiu Daoyin ◽  
Zhang Hongtao
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Vibe Algorithm

Improved Algorithm of the Video Moving Object Detection Based on ViBE

2020 ◽  
Vol 13 (4) ◽  
pp. 781-789
Author(s):  
Wang Zhongsheng ◽  
Lian Zhichao ◽  
Wang Yubian ◽  
Wang Jianguo
Keyword(s):  
Object Detection ◽  
Sudden Change ◽  
Moving Object Detection ◽  
Moving Object ◽  
Drastic Change ◽  
Light Change ◽  
Threshold Method ◽  
Original Algorithm ◽  
Vibe Algorithm ◽  
Improved Algorithm

Background: ViBE (Visual Background Extractor) is an algorithm with a variety of advantages in video moving object detection which utilizes a pixel-level background modeling. However, it is not suitable for distinguishing the scene of drastic change, adapts poorly to the sudden change of the illumination and may lost the object easily, because this algorithm uses a fixed threshold to distinguish the background from the foreground. Methods: In this paper, an improved ViBE algorithm is proposed, which an adaptive dynamic threshold method is introduced for classification of the foreground and the background in the changing scenes. When reconstructing the model it required for drastic change of illumination, Otsu algorithm is used to judge the threshold and select the appropriate frame to complete the reconstruction to achieve quick adapt to the light. Results: Experimental results show that the proposed algorithm has higher recall value, better precision and F value comparing to the original algorithm. The improved algorithm has the highest classification accuracy among other similar algorithms and therefore the improved algorithm significantly improves the detection results. Conclusion: After analyzing the principle of ViBE algorithm, this paper proposed improvements to it from two aspects to aim at its deficiency. Taking into account of the dynamic changes of different environments, the change factor was proposed to measure the dynamic degree of background. According to the value of the factor, adaptive clustering was obtained and clustering threshold was updated to improve the adaptability of the algorithm to the dynamic environments. The improved ViBE algorithm can find the appropriate frame to reconstruct model structure in the case of abrupt light change, which can quickly adapt itself to the light change and be more accurate in the object detection.

The method of rapid moving object detection in rotating camera

2013 ◽  
Vol 26 (7) ◽  
pp. 624-628
Author(s):  
Jianguo Jiang ◽  
Zhixiang Cai ◽  
Meibin Qi ◽  
Wei Wang
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Rotating Camera

scholarly journals Moving Object Detection by 3D Flow Field Analysis

2021 ◽  
Vol 22 (4) ◽  
pp. 1950-1963
Author(s):  
Cansen Jiang ◽  
Danda Pani Paudel ◽  
David Fofi ◽  
Yohan Fougerolle ◽  
Cedric Demonceaux
Keyword(s):  
Flow Field ◽  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Field Analysis ◽  
3D Flow ◽  
Flow Field Analysis
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