Objective

2

Effective detection of moving objects is a prerequisite in real-time tracking

behavior analysis

and behavioral judgment. Detection of moving objects has been widely applied in the fields of security

intelligent transportation

military

medical

and aerospace. Detection of moving objects is a key issue in the field of computer vision. The common methods used in the detection of moving objects include the optical flow

frame difference

and background subtraction methods. The optical flow method detects the moving region in the image sequence by using the vector feature of moving objects. This method performs well in the case of background motion. However

the computation of this method is complex and time consuming. The frame difference method uses the absolute value of difference between two adjacent frames to detect moving objects. The algorithm is simple and has good robustness. However

the detection results of this algorithm are easily affected by noises and is easy to produce "void". The background subtraction method is the most commonly used method in the detection of moving objects

in which the moving region is detected based on the difference between the current frame and background. The background subtraction method is simple

easy to implement

and can completely extract the object. The ViBe algorithm is a commonly used background subtraction method. This algorithm is mainly composed of background model initialization

foreground detection

and background model update

in which its concept is simple and easy to implement with high efficiency. However

a "ghost" phenomenon occurs in the ViBe algorithm when the initial frame contains moving objects and has the poor adaptability to noise

illumination

and dynamic environment. At the same time

the ViBe algorithm conducts foreground detection on each frame pixel-by-pixel

which has a large room for improvement regarding computational complexity. Thus

in this study

we improve the ViBe algorithm and propose the ViBeImp algorithm.

Method

2

In the ViBe algorithm

the construction of the background sample in the first frame of the video that contains the moving objects leads to the appearance of "ghost". Three solutions are used to solve this problem

where the first solution is by changing the manner of background model initialization

the second solution is by accelerating the elimination of the "ghost"

and the third solution is by combining with other methods to detect the "ghost" areas and deal with them. The acceleration of elimination of the "ghost" does not fundamentally eliminate the "ghost" and still leads to false detection in the initial part of the "ghost". The combination with other methods in detecting the "ghost" increases the complexity and the amount of calculation of the algorithm. Therefore

we modify the initialization of the background model to eliminate the "ghost". In the ViBeImp algorithm

the initial background used for the initial background sampling model is given by a multi-frame average method

which can eliminate the "ghost" to some extent. Different calculation methods of radius threshold directly affect the performance of foreground detection. A suitable radius threshold can be used to adapt to the changes in light and dynamic scenes in the video

which can effectively reduce the occurrence of missed detection and false detection. Thus

in the process of foreground detection

a self-adaptive calculation method of radius threshold is given by combining the background subtraction method

frame difference method

and OTSU algorithm. At the same time

foreground detection and model update are conducted only in the motion area

which are obtained based on the background difference method to reduce the computational complexity of the algorithm.

Result

2

First

the detection results of the ViBeImp algorithm

ViBe algorithm

ViBeDiff2 algorithm

and ViBeIniR algorithm in 25 different scenes that are selected from several public datasets

such as MOTChallenge

CDNET

and ViSOR

are provided with their corresponding precision

recall

and F1 values. Second

the improved detection results and effectiveness of the ViBeImp algorithm in the initialization background

adaptive radius threshold

and computational complexity are given. The ViBeImp algorithm improves the precision

recall

and F1 value of the ViBe algorithm by 13.73%

3.15%

and 9.44%

respectively

when the background initialization method differs from the ViBe algorithm. The ViBeImp algorithm improves the precision

recall

and F1 value of the ViBe algorithm by 11.14%

10.09%

and 12.35%

respectively

when the radius threshold calculation method differs from the ViBe algorithm. In terms of computational complexity

the average processing times per frame of the four methods are given

and 19 videos' average processing time per frame of the ViBeImp algorithm is lower than the ViBe algorithm in the 25 videos

which shows the effectiveness of our improvement in computational complexity. Finally

the comparison among the ViBeImp algorithm

Surendra algorithm

and Gaussian mixture model

which are commonly used in the detection of moving objects

is given. The experimental results show that the ViBeImp algorithm is robust to noise

illumination

and dynamic environment with complete detection results and better performance in real-time compared with the ViBe algorithm

ViBeDiff2 algorithm

ViBeIniR algorithm

Surendra algorithm

and Gaussian Mixture Model. Simultaneously

the ViBeImp algorithm improves the precision

recall and F1 of the ViBe algorithm by 17.98%

11.40%

and 15.96%

respectively.

Conclusion

2

The ViBeImp algorithm uses the multi-frame average method to construct the initial background that can effectively eliminate the "ghost". A self-adaptive calculation method of radius threshold is given

which enables the ViBe algorithm to rapidly adapt to the change of video environment and to accurately and completely detect the moving objects. The ViBe algorithm is a kind of algorithm with low false alarm and missed detection rates. The method overcomes the dependence of the ViBe algorithm on the initial background and video environment and reduces the computational complexity to a great extent with good robustness and applicability.