Page 33 - Read Online

Page 131 Tang et al. Intell Robot 2022;2(2):130­44 I http://dx.doi.org/10.20517/ir.2022.07

1. INTRODUCTION
The real-time detection of moving objects is an essential task in the computer vision field, which has wide ap-
plications, including target tracking, video surveillance, abnormal behavior analysis, intelligent robot, etc [1–5] .
There are still many challenges of the moving object detection under natural scenes, such as illumination
changes, swaying leaves, and shadow changes [6,7] . Therefore, it has attracted more and more attention from
researchers recently.

Therearemanyresearchachievementsinmovingobjectdetection. Forexample, SengarandMukhopadhyay [8]
[9]
proposed a motion detection method using block based bi-directional optical flow method. Chen et al. pro-
posed an end-to-end deep sequence learning architecture for moving object detection. Li et al. [10] presented
a novel technique for background subtraction based on the dynamic autoregressive moving average (ARMA)
model. These methods used for moving objects detection can be divided into three main types: the optical
flow method, the deep learning method, and the difference method. In addition, the difference methods are
further divided into three categories [11,12] , namely the unsupervised method [13] , the supervised method [14,15] ,
and the semi-supervised method [16,17] . There are some drawbacks in the optical flow method, such as com-
plex computation and sensitivity to illumination mutation, which is not suitable for real-time moving objects
detection [18] . Compared with traditional algorithms, deep learning methods have the advantages of high de-
tection accuracy and strong fitting ability, but the size of the dataset determines the effect of detection, and
it is difficult to meet the needs of deploying in some special scenarios at any time without sufficient samples.
At the same time, they have higher requirements on the hardware environment, so the computational cost of
deep learning-based algorithms is higher than that of traditional algorithms [19–21] . The background difference
method has become the most widely used method for its outstanding superiorities in computation complexity
and efficiency, which is the hot spot in moving object detection field [22] . However, the detection results of the
background difference method depend on the accuracy of the background model. The way of establishing a
robust background model is the key to this method.

There are many methods for moving object detection based on background difference methods, including
Gaussian single model (GSM), Gaussian mixture model (GMM), and visual background extractor (ViBe)
method [23,24] . ViBe algorithm is a sample-based moving object detection method, which has the advantages of
less calculation, small footprint, and fast processing speed. It is suitable for the real-time detection of moving
objects. Many researchers are focusing on the ViBe-based method of moving object detection. For example,
Talab et al. [25] proposed an approach for moving crack detection in video based on ViBe and multiple filter-
ing. Gao and Cheng [26] presented the use of the ViBe algorithm to extract smoke contours and shapes, which
finally makes the detection of smoke root more accurate. However, there are some deficiencies of the general
ViBe algorithm. For example, when the first frame of the video contains a moving object, there will be a ghost
area left in the current location, which will need a long time to be removed. In addition, there is often a shadow
problem in moving object detection based on the general ViBe algorithm.

To deal with the problems above for moving object detection based on ViBe method, various improvements
have been proposed. For example, Huang et al. [27] proposed a moving target detection algorithm based on the
improved ViBe algorithm by joining TOM (time of map) mechanism in the process of detection, where both
the spatial domain and the time domain information of the pixels were used to eliminate the ghost area. Qiu
et al. [28] presented a moving object detection method based on the strategy of ViBe algorithm and fused the
infrared imaging features, which can establish the pure background in a variety of complex conditions. Yue
et al. [29] introduced ant colony clustering algorithm and integrated it into the traditional ViBe framework and
extended the ViBe based on local modeling to a global modeling algorithm, which can deal with the target
adhesion problems but cannot effectively process shadows. The works above improve the performance of the
ViBe-based method to some extent. However, few of them considered the problems comprehensively. For
example, some methods considered the shadow problem, but they need a long computation time [30,31] .