A ViBe Based Moving Targets Edge Detection Algorithm and Its Parallel Implementation

Zhang, Han; Qian, Yurong; Wang, Yuefei; Chen, Renhe; Tian, Chenwei

doi:10.1007/s10766-019-00628-z

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The method detects moving objects in a video sequence, detects active patterns on a wide field of view, and moving objects removed from the video. Zhang H et al [12] proposed a method based on thread block coordinates, optimized divergence angle, computed kernel function, and CUDA (computing unified device architecture). The improved algorithm achieved ghosting elimination and avoided large irrelevant background edges and achieved better accuracy and precision.…”

Section: Related Workmentioning

confidence: 99%

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An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Liu,

Wei,

Yang

et al. 2023

Electronics

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Foreground detection is the main way to identify regions of interest. The detection effectiveness determines the accuracy of subsequent behavior analysis. In order to enhance the detection effect and optimize the problems of low accuracy, this paper proposes an improved Vibe algorithm combining the frame difference method and adaptive thresholding. First, we adopt a shallow convolutional layer of VGG16 to extract the lower-level features of the image. Features images with high correlation are fused into a new image. Second, adaptive factors based on the spatio-temporal domain are introduced to divide the foreground and background. Finally, we construct an inter-frame average speed value to measure the moving speed of the foreground, which solves the mismatch problem between background change rate and model update rate. Experimental results show that our algorithm can effectively solve the drawback of the traditional method and prevent the background model from being contaminated. It suppresses the generation of ghosting, significantly improves detection accuracy, and reduces the false detection rate.

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Section: Related Workmentioning

confidence: 99%

“…The calculation of the adaptive thresholding is shown as in Equation (12). where R (x, y) denotes the adaptive threshold, R(x, y) denotes the fixed threshold and δ is the set scale factor.…”

Section: Adaptive Thresholds In the Spatio-temporal Domainmentioning

confidence: 99%

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Liu,

Wei,

Yang

et al. 2023

Electronics

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show abstract

“…Xu et al [20] used optical flow to extract motion activation boxes from consecutive frames. Recently, Zhang et al [24] proposed an improved algorithm based on ViBe (visual background extraction) algorithm [25] to achieve moving target edge detection. In this paper, we employ You Only Look Once (YOLO) V3 [26], which is a deep learning framework based on Darknet-53 [27] and detects objects at three different scales, to mark the minimum circumrectangles of passengers.…”

Section: Motion Regions Marking and Optical Flow Extractionmentioning

confidence: 99%

Motion Direction Inconsistency‐Based Fight Detection for Multiview Surveillance Videos

Chong

Wang

et al. 2021

Wireless Communications and Mobile Computing

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Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

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Transprecision Gaussian Average Background Modelling Technique for Multi-vehicle Tracking Applications

Ilamathi

Sabitha

2022

Intelligent Communication Technologies and Virtual Mobile Networks

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A ViBe Based Moving Targets Edge Detection Algorithm and Its Parallel Implementation

Cited by 3 publications

References 11 publications

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Motion Direction Inconsistency‐Based Fight Detection for Multiview Surveillance Videos

Transprecision Gaussian Average Background Modelling Technique for Multi-vehicle Tracking Applications

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