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At the same time, to ensure the integrity of the foreground targets, the Canny edge detection is performed after
finding the different image between the current frame and the background frame [48] .

The whole work flow of the proposed approach for the moving objects detection is as follows:

Step1: Initialize the background model based on the mode method.

Step2: Convert the current frame and the background model to gray space, and then detect the foreground
objects which include shadows, based on the proposed ViBe algorithm with the adaptive detection radius
and update rate .

Step3: Convert HSV color space transformation for the current frame and detect the shadows by the color
invariance theory at the shadow and the background.

Step4: Carry out an “AND” operation on the results obtained from Steps 2 and 3 to remove the shadow of the
foreground targets.

Step5: Find the difference image between the current image frame and the background frame and perform
Canny edge detection.

Step6: Carry out an “OR” operation on the results obtained from Steps 4 and 5 to ensure the integrity of the
foreground objects.

4. RESULTS
To test the performance of the proposed approach, some experiments were carried out on several benchmark
datasets including Highway, Bungalow, Cars, and People [49,50] . These experiments were coded by Python on a
computer with 8G RAM and i7-4720HQ 2.60GHz CPU. Seven indices were used to evaluate the performance
ofdetection: recognitionrateofforeground(RE),recognitionrateofbackground(SP),falsepositiverate(FPR),
false negative rate (FNR), percentage of wrong classification (PWC), precision (PRE), and F-score (F) (see [51]
for the details of these indices). For these indices, the larger are the RE, SP, PRE, F, the more accurate is the
detected target area, and the smaller are the FPR, FNR, PWC, the more accurate is the detected background.
ThevaluesoftheparametersusedintheseexperimentsarethesameandlistedinTable1. Toshowtheefficiency
oftheproposedimprovedapproach(I-ViBe), itwascomparedwiththeGaussianmixturemodelbasedmethod
(GMM)andthegeneralViBe-basedmethod(G-ViBe). InthegeneralViBe-basedmethod,thedetectionradius
and the update rate are equal to 0 and 0 in the proposed approach.

4.1. The experiment for single object detection
To test the basic performance of the proposed approach, two experiments were conducted where only one
object was detected. The datasets used for this experiment were Walk (Clip1) and Bungalows (Clip2). Two
clips of the two videos were used to test the three detection methods, where the frame with the moving object
was used as the detection frame (see Figure 4b). The results of the two experiments are shown in Figure 4. The
evaluations for the three methods are listed in Table 2.

The results in Figure 4 show that all the three methods can detect the moving object effectively in this simple
experiment, and the results in Table 2 show that the proposed approach has better detection results in most
of the indices than the other two methods. In addition, the detection results on Walk (Clip1) show that the
general ViBe cannot deal with the ghost problem, while the proposed ViBe can remove the ghost area very
well. The detection results on Bungalows (Clip2) show that the proposed ViBe can remove the shadow more