Foreground detection based on modified ViBe in dynamic background

Xing-ming, 陈星明 Chen; Juan, 廖娟 Liao; Bo, 李勃 Li; Qi-mei, 陈启美 Chen

doi:10.3788/ope.20142209.2545

Cited by 9 publications

(2 citation statements)

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“…At present, dynamic scene modeling is used to detect moving objects in complex motion scenes. Common feature dynamic scene modeling methods include the hybrid Gaussian modeling algorithm, the Bayesian background modeling algorithm, the non-parametric kernel density estimation method, and the ViBe scenario modeling method [48][49][50]. However, the aforementioned modeling methods are not applicable when the entire Region of Interest (ROI) is in motion.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Ma,

Meng

2023

Sensors

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In the production process of progressive die stamping, anomaly detection is essential for ensuring the safety of expensive dies and the continuous stability of the production process. Early monitoring processes involve manually inspecting the quality of post-production products to infer whether there are anomalies in the production process, or using some sensors to monitor some state signals during the production process. However, the former is an extremely tedious and time-consuming task, and the latter cannot provide warnings before anomalies occur. Both methods can only detect anomalies after they have occurred, which usually means that damage to the die has already been caused. In this paper, we propose a machine-vision-based method for real-time anomaly detection in the production of progressive die stamping. This method can detect anomalies before they cause actual damage to the mold, thereby stopping the machine and protecting the mold and machine. In the proposed method, a whole continuous motion scene cycle is decomposed into a standard background template library, and the potential anomaly regions in the image to be detected are determined according to the difference from the background template library. Finally, the shape- and size-adaptive descriptors of these regions and corresponding reference regions are extracted and compared to determine the actual anomaly regions. The experimental results indicate that this method can achieve reasonable accuracy in the detection of anomalies in the production process of stamping progressive dies. The experimental results demonstrate that this method not only achieves satisfactory accuracy in anomaly detection during the production of progressive die stamping, but also attains competitive performance levels when compared with methods based on deep learning. Furthermore, it requires simpler preliminary preparations and does not necessitate the adoption of the deep learning paradigm.

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Section: Introductionmentioning

confidence: 99%

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Ma,

Meng

2023

Sensors

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“…At present, dynamic scene modeling is used to detect moving objects in complex motion scenes. Common feature dynamic scene modeling methods include the hybrid Gaussian modeling algorithm, the Bayesian background modeling algorithm, the non-parametric kernel density estimation method, and the ViBe scenario modeling method [18][19][20]. However, because the detected ROI region is completely mobile, these methods have a high detection error rate and low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in periodic motion scenes based on multi-scale feature Gaussian weighting analysis

Wang¹,

Meng²,

Huang³

et al. 2019

Meas. Sci. Technol.

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Anomaly monitoring of production line processing equipment based on machine vision is an important method for ensuring its efficient and stable operation. However, problems related to dynamic scenes, accidental and non-transcendental anomalies, image vulnerability to the severe vibrations of machining equipment, and difficulty in accepting the missing detection are significant obstacles to abnormality monitoring in the machining process. A periodic motion scene decomposition method is presented in this paper to solve dynamic scenes, occasional anomalies, severe vibrations, and other issues. Through optimization of the morphological structural elements, the feature points of the 'abnormality' region are obtained, and a Gaussian weighting formula is derived to detect the anomaly and improve the accuracy of detection. This method, which is verified by experiments, effectively overcomes problems related to the machining process and achieves good detection results.

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MCSHM: A simple and practical method for moving objects detection in dynamic scenes

Guo

2017

2017 Chinese Automation Congress (CAC)

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Foreground detection based on modified ViBe in dynamic background

Cited by 9 publications

References 0 publications

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Anomaly detection in periodic motion scenes based on multi-scale feature Gaussian weighting analysis

MCSHM: A simple and practical method for moving objects detection in dynamic scenes

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