Objective

2

Moving target detection is an important branch of image processing and computer vision

and it is also a core part of intelligent monitoring systems. Its main content is to observe the entire scene in the video sequences and find the moving targets. Therefore

the main purpose of moving target detection is to extract the moving target from the video sequences effectively and obtain the feature information of the moving target

such as color

shape

and contour. Extracting moving targets is the process of target and background classification. The process finds the difference by successive sequences of images and extracts the differences owing to the motion of the object to obtain the desired target. Moving target detection requires fast acquisition of moving targets in the video image and

as much as possible

to ensure the integrity of the acquired moving targets. Thus

speed and integrity are two key indicators of moving target detection algorithms. In terms of rapidity

algorithms are required to have lower complexity and can detect moving targets in real time. The existing algorithms that satisfy speed are easily affected by illumination

have weak adaptability to the dynamic environment

and the acquired target information is incomplete

thereby resulting in a hole problem. The internal integrity of the target and the integrity of the target contour are required

thereby indicating that the internal information of the moving target can be fully obtained

and the phenomenon of missed detection caused by the misidentification of the foreground area as the background in the detection is eliminated. At the same time

the target edges are as continuous and smooth as possible. However

the algorithm with improved integrity has high complexity

slow operation speed

and poor real-time performance. Therefore

achieving the balance between speed and integrity has become a key issue in moving target detection

causing the algorithm to have high extraction efficiency while fully extracting the internal information and contour of the target.

Method

2

This study proposes a three-frame difference algorithm based on adaptive Gaussian mixture modeling. To ensure the real-time performance of the algorithm

this study relies on the three-frame difference operation

which is simple

extensible

and has good anti-interference ability to extract the target contour of the video image. The operation can improve the detection efficiency of the algorithm. For the problem that the three-frame difference operation leads to incomplete extraction of the internal information of the target

the Gaussian mixture background difference adaptively adjusted by the learning rate is used. The difference achieves an adaptive update of the background model by setting the frame number threshold and adopting different learning rates before and after the threshold. At the beginning of the model creation

the rate of iteration of the background model is increased by the faster update rate of the model

and the "ghosting" caused by the motion of the object is eliminated. After the interference information in the background model is eliminated

the learning rate is adjusted based on the difference between the target pixel and adjacent eight pixels in the current frame and the background model

thereby implementing adaptive correction of the background model and solving the problem of misjudgment and loss of targets generated during the model update process. The approach can increase the integrity of the target image. At the same time

to speed up the Gaussian mixture modeling

the model redundancy decision strategy is adopted to determine the weight and priority of the Gaussian distributions

and the redundant Gaussian distributions are deleted to avoid the time consumption caused by the redundancy models in the matching. Ultimately

the balance between algorithm integrity and algorithm real-time are achieved. To further ensure the integrity and continuity of the target edge

we use the edge contrast difference algorithm

which is based on the target edge detected by the Canny operator. The number of frames participating in the edge contrast operation is adaptively selected based on the target motion speed

thereby decreasing the false positive rate of the background point and making the edge information as continuous and complete as possible.

Result

2

Subjective and objective evaluation methods are combined on the experimental results. Subjectively

the background difference based on Gaussian mixture modeling (BD-GMM)

the three-frame difference based on edge contrast (TFD-EC)

and the proposed algorithm are used to detect single-target and multi-target video in different backgrounds. The results show that the target information obtained by the algorithm are complete and the edges are smooth. Objectively

the proposed algorithm improves the detection rate while ensuring a high accuracy rate of 95.23%

and the integrity of the target is improved by 28.95%. These values are significantly higher than those of other algorithms. In terms of speed

the time consumption is reduced by 29.18% compared with that of the traditional Gaussian mixture algorithm

thereby meeting the real-time requirements. Compared with the BD-GMM and TFD-EC algorithms

both subjective and objective

the proposed algorithm is superior to the two algorithms.

Conclusion

2

The experimental results show that because the algorithm adopts Gaussian mixture background modeling based on adaptive learning rate

it can effectively suppress the interference of a dynamic environment and decrease the complexity of the algorithm. The three-frame difference algorithm based on edge comparison ensures the timeliness of the algorithm and integrity of the target edge. Therefore

the proposed algorithm ensures real-time performance

has good integrity

and can be widely used in fields such as intelligent video surveillance

military applications

industrial inspection

and aerospace.