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Tang et al. Intell Robot 2022;2(2):130­44 I http://dx.doi.org/10.20517/ir.2022.07 Page 136

Here, is the number of pixels. ( , ) is the difference of the pixels between two images +1 ( , ) and
( , ), namely
{
0, | +1 ( , ) − ( , )| < 1
+1 ( , ) = (9)
1, Otherwise
where 1 is a threshold to reduce the effects of the moving objects.

Remark: The mode background method can eliminate the foreground target that appears in the previous
frames. The subsequent frames of the video sequence continuously update the “ghost” area to set it as the
background, which can effectively speed up the “ghost” area removal.

3.3. Shadow removal strategy
Shadow is a common problem in moving object detection, and how to remove the shadow is a hot topic in
the field of computer vision [45,46] . In this paper, an improved method based on the HSV color space is used
to complete the shadow removal task. The main reason for using the HSV color space is that it is very close to
the characteristics of human vision considering the existing methods, which is more accurate than RGB color
space for shadow removal. However, there are many parameters of the traditional HSV that need to be set in
differentvideoenvironments,suchasthethresholdsusedfortheshadowjudgment [47] . Inaddition,whenthere
is no significant difference on the color attribute between the moving object and the shaded area, the accuracy
of shadow removal based on the traditional HSV color space will be decreased. To deal with these problems,
an improved shadow removal strategy is proposed in this paper. The basic idea of the proposed method is that
the shadow area can be effectively distinguished by using the characteristics of shadow intensity reduction and
color invariance theory, because the HSV color space can directly reflect the color characteristics of the image.
The main procedures of the proposed method are as follows:

(1) The HSV space transformation is done. Then, the values of the , , and components are obtained.
Since the value is a direct measure of the brightness of the color, the brightness of these pixels is significantly
reducedintheshadowpart. Thedifferenceofthebrightnessisdenotedas ( , ), whichisdefinedasfollows:

( , ) = ( , )/ ( , ) (10)
where ( , ) is the value of current image frame. ( , ) is the value of background frame. For any
pixelpoints ( , ),thebrightnessvaluebetweenthecurrentframeandbackgroundframeisusedtodetermine
whether the current pixel is a shadow point. The decision strategy is as follows:
{
1, 2 ≤ ( , ) ≤ 3
2( , ) = (11)
0, Otherwise
where 2( , ) is a flag. 2 and 3 are two thresholds for shadow detection.

(2) When the chromaticity of the object is similar to the shadow, the shadow area will be enlarged based on
the brightness detection above. To deal with this problem, an improved method is proposed based on the
forming mechanism of shadow. Namely, for each shadow pixel ( , ), its darkness level is limited, because it
is darkened for the blocking out of the illumination source, but there is the presence of ambient illumination.
In addition, the shadow pixels are mostly in gray areas. The decision strategy is as follows:
{
1, ( , ) ≤ 1 and ( , ) ≥ 2
3( , ) = (12)
0, Otherwise
where 3( , ) is a flag; ( , ) is the saturation of the pixel point of current image frame; 1 is the maxi-
mum value of the saturation in the gray range; and 2 is the minimum value within the gray range. Then, the
shadow area can be detected by:
{
1, 2( , ) = 1 and 3( , ) = 1
( , ) = (13)
0, Otherwise