Page 34 - Read Online

Tang et al. Intell Robot 2022;2(2):130­44 I http://dx.doi.org/10.20517/ir.2022.07 Page 132

In this paper, an improved ViBe-based approach is proposed, where the problems of moving object detection
under natural scenes are fully considered including the ghost area problem, the dynamic background problem,
and the shadows problem, and some solutions are presented. Finally, various experiments were conducted
under different scenes for moving object detection task. The results show the efficiency and effectiveness of
the proposed approach.

The main contributions of this paper are summarized as follows: (1) A new background model based on
mode background modeling method is proposed to eliminate the ghost areas quickly; (2) An improved ViBe
approach is proposed based on an adaptive foreground detection and background updating method, where
the value of the eight neighboring pixels difference between the background and the current frame is used. (3)
A novel shadow elimination approach is presented, which is based on the HSV color space combined with
the edge detection method. Furthermore, the computation time and background updating mechanism of the
proposed approach are discussed.

This paper is organized as follows. Section 2 provides the related works about the ViBe-based method. Sec-
tion 3 presents the improved ViBe-based method for moving object detection. The moving object detection
experiments under various natural scenes are given in Section 4. Section 5 discusses the performance of the
proposed approach. Finally, the conclusions are given in Section 6.

2. RELATED WORKS
In the past few years, various foreground target detection methods have been proposed to build powerful and
flexible background models that can be used in surveillance scenarios with different challenges. One of the
most widely used probabilistic models is the GMM [32] , which models each pixel using a mixture of Gaussian
models rather than modeling all pixel values as a distribution. For example, Kaewtrakulpong and Pakorn [33]
modified the update equation of GMM for improving the accuracy and proposed a shadow detection scheme
based on the existing GMM. Hofmann et al. [34] used a constantly adapted number of Gaussian distributions
of the GMM for each pixel.

As for nonparametric approaches, Barnich and Droogenbroeck [35,36] proposed the ViBe-based method, where
the current pixel value is compared to its closest sample within the collection of samples. First, the pixel values
of the detected frames are matched with the corresponding models. The threshold value determines whether
it belongs to the background or the foreground; for the matching pixel, the background model of the pixel and
its neighborhood is updated by a random update mechanism. The method is simple to operate and detects
well in static backgrounds but has fixed parameters. This limits the algorithm’s ability to adapt to dynamic
backgrounds (surface ripples, leaf shaking, etc.), and its neighborhood diffusion update strategy causes slower-
moving foreground targets to blend into the background too quickly, increasing false detections. Its single-
frame input image initialization strategy creates a “ghost” area when the input image contains foreground
targets. In addition, there is often a shadow problem in moving object detection based on the general ViBe
algorithm, which affects the accuracy of the background model.

To deal with the problems above for moving object detection based on ViBe method, various improvements
have been proposed. For example, Zhu et al. [37] proposed a fast and efficient improvement of ViBe algorithm
based on the edge characteristic info and neighborhood mean filter, but there are a lot of holes inside the detec-
tion area. Chen et al. [38] combined physical shadow theory and C1C2C3 color space for the shadow removal.
Yang et al. [39] used two thresholds to describe the uncertainty in the ViBe-based color video detection, and
they used evidence theory to model and handle the uncertainty. Liu et al. [40] used the temporal and spatial
information of the pixels to initialize the background model, and then combined the background sample set
with the neighborhood pixels to determine the complexity of the background and obtain an adaptive segmen-