The improvement of VIBE foreground detection algorithm

2018 ◽  
Author(s):  
Daimeng Zhang ◽  
ahong xu ◽  
Jun Zhang ◽  
Jinwen Tian ◽  
Xiaomao Liu
Keyword(s):  
Detection Algorithm ◽  
Foreground Detection

scholarly journals Video Foreground Detection Algorithm Based on Fast Principal Component Pursuit and Motion Saliency

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Chen ◽  
Ying Tong ◽  
Jie Yang ◽  
Minghu Wu
Keyword(s):  
Principal Component ◽  
Detection Algorithm ◽  
Low Rank ◽  
Foreground Detection ◽  
Dynamic Background ◽  
Foreground Region ◽  
Principal Component Pursuit ◽  
Small Targets ◽  
Background Motion ◽  
Motion Saliency

Aiming at the shortcoming of being unsuitable for dynamic background and high computational complexity of the existing RPCA- (robust principal component analysis-) based block-sparse moving object detection method, this paper proposes a two-stage foreground detection framework based on motion saliency for video sequence. At the first stage, the observed image sequence is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA method via fast PCP (principal component pursuit). At the second stage, the sparse foreground blocks are obtained according to the spectral residuals and the spatial correlation of the foreground region. Finally, the block-sparse RPCA algorithm through fast PCP is used to estimate foreground areas dynamically and to reconstruct the foreground objects. Extensive experiments demonstrate that our method can exclude the interference of background motion and change, simultaneously improving the detection rate of small targets.

scholarly journals Precise Foreground Detection Algorithm Using Motion Estimation, Minima and Maxima Inside the Foreground Object

2013 ◽  
Vol 59 (4) ◽  
pp. 725-731 ◽  
Author(s):  
Muhammad Nawaz ◽  
John Cosmas ◽  
Pavlos I. Lazaridis ◽  
Zaharias D. Zaharis ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Detection Algorithm ◽  
Foreground Detection ◽  
Foreground Object

scholarly journals Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meiman Li ◽  
Wenfu Xie
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Motion Path ◽  
Support Vector ◽  
Foreground Detection ◽  
Surveillance Video ◽  
Detection Range ◽  
Window Method ◽  
Fusion Detection

For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. Firstly, the motion region is extracted by the method of interframe difference and background difference. Then, the normalized motion region extracts the feature vectors based on the improved YOLOv3 tiny network. Finally, the trained linear support vector machine is used for pedestrian detection, and the performance of the fusion detection algorithm on caviar dataset is given, which proves the effectiveness of the proposed fusion detection algorithm. Experimental results show that the proposed method not only improves the practical application of pedestrian rerecognition but also reduces the detection range, computational complexity, and false detection rate compared with sliding window method.

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