Foreground Object Detecting Algorithm based on Mixture of Gaussian and Kalman Filter in Video Surveillance

Wang, Xun; Sun, Jie; Peng, Haoyu

doi:10.4304/jcp.8.3.693-700

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…Every Gaussian model maintains the mean and the variance of each pixel, with the assumption that each pixel value follows a Gaussian distribution. If considering the swaying of a tree or the flickering of a monitor, the MoG can handle those multi-model changes quite well [10]. Many improvements on Gaussian mixture model have been implemented like MOG2.…”

Section: Adaptive Background Subtractionmentioning

confidence: 99%

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An Image Feature-Based Method for Parking Lot Occupancy

Tatulea¹,

Calin²,

Brad³

et al. 2019

Future Internet

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The main scope of the presented research was the development of an innovative product for the management of city parking lots. Our application will ensure the implementation of the Smart City concept by using computer vision and communication platforms, which enable the development of new integrated digital services. The use of video cameras could simplify and lower the costs of parking lot controls. In the aim of parking space detection, an aggregated decision was proposed, employing various metrics, computed over a sliding window interval provided by the camera. The history created over 20 images provides an adaptive method for background and accurate detection. The system has shown high robustness in two benchmarks, achieving a recognition rate higher than 93%.

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Section: Adaptive Background Subtractionmentioning

confidence: 99%

“…The system involves the use of a Gaussian mixture model to adjust the probability density function (PDF) of each pixel X(x, y) and create the background model. The probability of observing the value of a pixel is defined as follows [10]:…”

Section: Adaptive Background Subtractionmentioning

confidence: 99%

An Image Feature-Based Method for Parking Lot Occupancy

Tatulea¹,

Calin²,

Brad³

et al. 2019

Future Internet

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show abstract

“…In the field of intelligent video surveillance [4] [5], there are some research results about crowd density estimations and counting. Currently, there are two methods for crowd density estimation.…”

Section: Introductionmentioning

confidence: 99%

Crowd Density and Counting Estimation Based on Image Textural Feature

Yang

2014

JMM

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This paper proposes an image textural analytical method for estimating the crowd density and counting. At first, the target detection is conducted to obtain the foreground image. This crowd image is used to calculate the gray level co-occurrence matrix (GLCM). Then, according to the characteristic values of the gray level co-occurrence matrix, i.e., energy, entropy, contrast, homogeneity, we use support vector machine (SVM) to estimate crowd density. Simultaneously, the method of linear regression is used to estimate the crowd counting. The accuracy of evaluation is improved since we extract the target image textural traits to overcome the influence of background for estimation results. Finally, the experimental results show that the proposed approaches of crowd density and counting are feasible and effective.

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“…The process of acquiring the parameters of camera geometric model is called camera calibration [1]. It is an essential step to extract three-dimensional space information from two-dimensional images in the applications of image processing and computer vision, and is widely used in three-dimensional reconstruction, navigation, visual surveillance and other fields [2][3]. Under certain camera model, camera calibration strikes camera parameters of the model with a series of mathematical transformations and calculations by virtue of the image processing.…”

Section: Introductionmentioning

confidence: 99%

A Novel Stratified Self-calibration Method of Camera Based on Rotation Movement

Li¹,

Su²

2014

JSW

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This paper proposes a novel stratified selfcalibration method of camera based on rotation movement. The proposed method firstly captures more than three images of the same scene in the case of constant internal parameters by panning and rotating the camera with small relative rotation angles among the captured images. After feature extraction and matching of captured images, the pixel coordinates of feature point are normalized. Then the stratified self-calibration is performed, following projective, affine and metric calibration. Projective calibration determines the camera projective matrix of every image in the projective reconstruct space. Affine calibration calculates the parameters of infinity reference plane in this space and the homography according to approximately equal relationship among the main diagonal elements of homography, which is inferred by virtue of small relative rotation angles among the captured images and the property of internal reference matrix corresponding to normalized pixel coordinates. Lastly metric calibration acquires internal reference matrix from the calculated homography. The proposed method can be online applied to simply, fast, accurately obtain internal parameters of camera without using the calibration reference with known 3D information. Real data has been used to test the proposed approach, and very good results have been achieved.

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Foreground Object Detecting Algorithm based on Mixture of Gaussian and Kalman Filter in Video Surveillance

Cited by 9 publications

References 27 publications

An Image Feature-Based Method for Parking Lot Occupancy

An Image Feature-Based Method for Parking Lot Occupancy

Crowd Density and Counting Estimation Based on Image Textural Feature

A Novel Stratified Self-calibration Method of Camera Based on Rotation Movement

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