Background Subtraction Based on Gaussian Mixture Model

2013 ◽  
Vol 694-697 ◽  
pp. 2021-2026
Author(s):  
De Fang Liu ◽  
Ming Deng ◽  
Dai Mu Wang
Keyword(s):  
Probability Density ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Adaptive Algorithm ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Video Sequences ◽  
Recursive Equations ◽  
Gauss Mixture

According to the detection of moving objects in video sequences, the paper puts forward background subtraction based on Gauss mixture model. It analyzes the usual pixel-level approach, and to develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

Moving Object Detection and Tracking in outside Environments

2011 ◽  
Vol 130-134 ◽  
pp. 3862-3865
Author(s):  
Yi Ding Wang ◽  
Da Qian Li
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Middle Ground ◽  
Moving Objects Detection ◽  
Detection And Tracking ◽  
Objects Detection ◽  
Typical Method

Background subtraction is a typical method for moving objects detection. The Gaussian mixture model is one of widely used method to model the background. However, in challenge environments, quick lighting changes, noises and shake of background can influence the detection of moving objects significantly. To solve this problem, an improved Gaussian Mixture Model is proposed in this paper. In the proposed algorithm, Objects are divided into three categories, foreground, background and middle-ground. The proposed algorithm is a segmented process. Moving objects including foreground and middle-ground are extracted firstly; then foreground is segmented from middle-ground. In this way almost middle-ground are filtered, so we can obtain a clear foreground objects. Experimental results show that the proposed algorithm can detect moving objects much more precisely, and it is robust to lighting changes and shadows.

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

Modified Object Tracking and Counting Method Based on Gaussian Mixture Model

2013 ◽  
Vol 373-375 ◽  
pp. 598-602 ◽  
Author(s):  
Ming Jie Zhang ◽  
Bao Sheng Kang
Keyword(s):  
Object Tracking ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Video Sequences ◽  
Counting Method ◽  
Centroid Distance ◽  
Monocular Video ◽  
Video Scene ◽  
Detection Ratio

In a monocular video scene, in order to improve the efficiency of object tracking and counting under occlusion conditions. The article presents a scheme to automatically track and count people in a surveillance system. First, a modified Gaussian mixture model was employed to determine pedestrian objects from a static scene. To identify foreground objects by positions and sizes of foreground regions which were obtained. Moreover, the performance to track objects was improved by using the modified overlap tracker, the modified overlap tracker was used to analyze the centroid distance between neighboring objects and help object tracking and people counting in occlusion states of merging and splitting. On the experiments of tracking and counting people in three video sequences, the results show that the proposed method can improve the averaged detection ratio about 10% as compared to the conventional work.

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