Page 43 - Read Online

Page 141 Tang et al. Intell Robot 2022;2(2):130­44 I http://dx.doi.org/10.20517/ir.2022.07

Table 5. The moving object detection experiments based on different background discrimination mechanism

The valuation indices G-ViBe [35] F-ViBe I-ViBe
SP 0.8765 0.9738 0.9960
RE 0.7496 0.7394 0.7184
FPR 0.1234 0.0261 0.0039
FNR 0.2503 0.2605 0.2815
PWC 0.1285 0.0328 0.0106
PRE 0.2016 0.4551 0.8202
F 0.3178 0.5634 0.7659

Table 6. The moving object detection experiments based on different background discrimination mechanism

The video Computation time (s) GMM [32] G-ViBe [35] I-ViBe
Clip1 of Section 4.1 background modeling 0.4042 1.9662
(180 ∗ 144) object detection 0.0337 0.1028 0.1508
Clip1 of Section 4.2 background modeling 0.4077 2.0265
(320 ∗ 240) object detection 0.0353 0.1049 0.2503
Clip1 of Section 4.3 background modeling 0.4966 2.2822
(720 ∗ 480) object detection 0.0798 0.1229 0.4138

The results in Table 6 show that more time for the object detection is needed using G-ViBe and I-ViBe than the
GMM method, because the GMM method selects the initial background frame randomly. For high resolution
videos, the proposed ViBe method takes more time to compare the values of pixels in each channel of the HSV
space, so the time for object detection increases. In addition, the results show that more time is needed in
the ViBe based approach during the background modeling process, which can be off-line proceeded and will
not affect the real-time moving object detection. For off-line processing, multiple images of the detection area
can be collected in advance, and the mode background method can be used for modeling. In the subsequent
detection tasks, there is no need to repeat the modeling. Thus, the proposed approach has a better comprehen-
sive performance than both the GMM method and the G-ViBe method, although the computation time of the
proposed approach is relatively higher than the other two methods, which is a problem for further study.

6. CONCLUSIONS
In this paper, we present an improved moving object detection approach based on ViBe algorithm. During
the process of foreground region extraction, the initial background is obtained by the previous few frames
and then updated by the value of the eight neighboring pixel difference between the background and the
current frame. In addition, a shadow removal strategy is adopted by combining the HSV color space and the
edge information. Most of the parameters in the proposed method are calculated adaptively, which is very
important for the adaptivity of moving object detection method. The experiments showed that the proposed
approach can deal with moving object detection efficiently in various situations, such as the severe shadow
problems in the foreground and the presence of moving objects in the first frame. In addition, the proposed
approach can be used for real-time moving object detection. In future work, some more efficient methods
based on artificial intelligence algorithms should be studied to improve the accuracy and real-time ability for
moving object detection.