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Aiming at the problems that the classical Gaussian mixture model is unable to detect the complete moving object, and is sensitive to the light mutation scenes and so on, an improved algorithm is proposed for moving object detection based on Gaussian mixture model and three-frame difference method. In the process of extracting the moving region, the improved three-frame difference method uses the dynamic segmentation threshold and edge detection technology, and it is first used to solve the problems such as the illumination mutation and the discontinuity of the target edge. Then, a new adaptive selection strategy of the number of Gaussian distributions is introduced to reduce the processing time and improve accuracy of detection. Finally, HSV color space is used to remove shadow regions, and the whole moving object is detected. Experimental results show that the proposed algorithm can detect moving objects in various situations effectively.

The performance of moving object detection algorithm is very crucial in a sound surveillance system for reliable tracking and behavior recognition. In recent years, many scholars have successively proposed lots of classic moving object detection algorithms [

Stauffer et al. proposed Gaussian mixture model (GMM) [

Based on Gaussian mixture model and three-frame differencing method, we propose a moving object detection algorithm in this paper. The experiments show that our algorithm has improved in the aspects of accuracy, adaptability and real-time performance.

GMM algorithm considers the value of a particular pixel

where

where n is dimension of the

where

where

The Gaussian distributions are ordered by the value of

where T is a measure of the minimum portion of the pixel that should be accounted for by the background. Each pixel which is matched with any of the former B Gaussian distributions will be marked as background pixel. Otherwise, it will be marked as foreground pixel.

This paper proposes a moving object detection algorithm which based on Gaussian mixture model and three- frame difference method. Firstly, we achieve the coarse segmentation of motion region which utilize the improved three-frame difference method. We make up the target boundary fracture portions by combining with edge detection technology, and overcome the impact of the sudden illumination change by adjusting the binary threshold value based on changes of the scenes. Secondly, in the process of mixture Gaussian background model, according to the change of model’s weight decide to add model or remove unmatched model, the number of components of the mixture is constantly adapted for each pixel, which improves the ability of the algorithm to describe the scene. Finally, we utilize characteristic of HSV color space to eliminate the interference of the shadow region effectively.

The traditional three-frame difference method can quickly detect moving objects. But the algorithm has poor ability to deal with illumination change scenes because of the fixed segmentation threshold it utilized. In addition, holes phenomena will occur in object interior and part of the object contour is not continuous. To solve these problems, this paper proposes the improved of three-frame difference method. It combines three-frame difference method with canny edge detection [

1) Three frames, defined as

where T is the fixed binary threshold,

where

2) We get edge image

3) We obtain complete moving object region

When

Moving target detection algorithm based on Gaussian mixture model basically set the fixed number of Gaussian distributions for each pixel. In fact, when recently observed pixel values are roughly constant, all of the distributions approximate the same values. In such a case, only one distribution should exist and the other distributions are not necessary at all. On the contrary, if recently observed pixel values change frequently, a constant number of Gaussian is not always enough to estimate the background model, and which is very difficult to determine the appropriate number of Gaussians. Therefore, this paper proposes a new background estimation method, which can increase and decrease the number of distributions to handle the variations of each pixel. The update process of improved Gaussian mixture model is listed as follows:

1) Increment of distribution

Every new pixel value

2) Decrement of distribution

In the process of Gaussian mixture model update, if a Gaussian distribution of the current pixel can’t describe the background accurately, its weight will continue to decay according to

3) The integrated Gaussian distribution

When the difference between means of two Gaussians (the one is

Shadow is the dark area in which the light source can’t directly irradiate to the surface of the object, and it can be divided into two types: shadows of the object and the cast shadows [

where

In this paper, all experiments are performed on windows 7 system with 3.00 GHz Core 4 processor and 4.00 GB of memory. In order to analyze the robustness and effectiveness of the proposed method, four experiments under different conditions are demonstrated in our paper. The results are compared with GMM algorithm and the algorithm [

Algorithms | GMM algorithm | Algorithm [ | Our algorithm |
---|---|---|---|

Running time(ms/f) | 82 | 58 | 50 |

videos | GMM algorithm | Algorithm [ | Our algorithm | |||
---|---|---|---|---|---|---|

Recall | Precision | Recall | Precision | Recall | Precision | |

a b c d e f g | 84.73 85.31 80.42 72.43 74.93 90.34 96.65 | 82.52 83.72 75.74 70.58 72.25 40.55 32.23 | 91.55 90.26 86.58 87.49 88.47 84.15 88.72 | 90.32 87.67 85.45 86.56 85.52 82.37 86.38 | 93.25 94.15 89.37 90.82 91.55 92.82 94.18 | 91.33 89.81 87.51 89.71 90.12 91.54 92.39 |

detect the moving objects accurately and completely.

In order to systematically evaluate various algorithms, it is useful to identify the following two important quality measures: Recall (also known as detection rate) and Precision (also known as positive prediction) [

where

We calculate Recall and Precision of various algorithms, and results are reported in

This paper proposes an improved moving object detection algorithm based on adaptive Gaussian mixture model and three-frame difference method. The proposed algorithm can automatically select the number of components for each pixel. This modification dramatically improves the convergence and the accuracy of background subtraction whilst maintaining the same temporal adaptability. The three-frame difference method uses adaptive segmentation threshold that can adapt to illumination change scene. In addition, we preserve the complete edge information of moving object by using edge detection technology, and effectively remove the shadow by using HSV color space. Comprehensive analysis of experimental results shows that our algorithm can detect moving objects in the complex scenes effectively and has good robustness.

Xuegang Hu,Jiamin Zheng, (2016) An Improved Moving Object Detection Algorithm Based on Gaussian Mixture Models. Open Journal of Applied Sciences,06,449-456. doi: 10.4236/ojapps.2016.67045