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Tang et al. Intell Robot 2022;2(2):130­44 I http://dx.doi.org/10.20517/ir.2022.07 Page 140

Table 4. The valuation of the three methods for moving object detection under challenging conditions

The valuation The video Clip1 The video Clip2
indices GMM [32] G-ViBe [35] I-ViBe GMM [32] G-ViBe [35] I-ViBe
SP 0.9547 0.8765 0.9960 0.9850 0.9025 0.9974
RE 0.8755 0.7496 0.7184 0.8643 0.9258 0.9574
FPR 0.0452 0.1234 0.0039 0.0149 0.0974 0.0025
FNR 0.1244 0.2503 0.2815 0.1356 0.0741 0.0425
PWC 0.0481 0.1285 0.0106 0.0219 0.0957 0.0051
PRE 0.4245 0.2016 0.8202 0.7819 0.4173 0.9617
F 0.5718 0.3178 0.7659 0.8210 0.5753 0.9595

(a) (b) (c) (d) (e) (f)

Figure 7. The moving object detection experiments on the video Fall: (a) the first frame; (b) the frame for detection; (c) the ground-truth;
d) the result of G-ViBe; (e) the result of F-ViBe; and (f) the result of I-ViBe.

namicenvironments. Themainreasonisthatallthethreemethodsarebasedonthemechanismofbackground
subtraction. However, the performance of the proposed approach does not decrease dramatically compared
with other two methods (see the values of PRE and F in Table 4). This performance of the proposed approach
is very important for the real application of moving object detection.

5. DISCUSSION
The results presented in Section 3 show that the proposed approach can deal with the ghost area problem and
remove the shadow very well. In addition, the evaluation indices of the proposed approach are better than the
GMM method and the general ViBe method. In this section, some performances of the proposed approach
are discussed, including the computation complexity and the background updating mechanism.

One key part of the ViBe-based approach is the background updating mechanism, so the performance of the
improvement in this part for the proposed method is discussed first. An experiment was conducted in the
dataset of Fall, where the proposed approach was compared with two methods. The first one is the general
ViBe. The second one is a method which has the same parameters and work flow as the proposed approach,
except that the background updating mechanism is based on the fixed detection radius and updating rate,
and this method is called F-ViBe. The experimental results of Section 3.3 are used as reference, as shown in
Figure 7 and Table 5. The experimental results show that the proposed approach can deal with the dynamic
environment better than the other two methods. Thus, the background updating mechanism is very efficient
for moving object detection under complex environment. In addition, the detection radius and updating rate
of the F-ViBe method are given by the designer, which need more experience and time.

Another important index of the moving object detection method is the real time problem, because the speed of
the moving object is very high sometimes. The proposed approach has two main differences with the general
ViBe method, the background modeling and updating mechanism and the shadow removal strategy. Thus,
the time needed in all the three experiments of Clip1 in Section 3 is divided into two parts, the time for the
background modeling and the time for moving object detection (including background updating).