# Foreground Objects Detection by U-Net with Multiple Difference Images
## 2 Related Works
### Earlier Approaches  
[PERSON] and [PERSON] [9] proposed a method called mixture of Gaussian (MOG) that represents the brightness value of each pixel as the combination of Gaussian distributions. They suggested a method to determine the number of the Gaussian mixture and each parameter of the Gaussian distribution using the expectation and maximization algorithm [10]. No special initialization is required because it adapts their parameters as a sequence goes on. Pixels are considered as background when their brightness values belong to the Gaussian mixture model, otherwise, they are considered as foreground. [PERSON] et al. [11] proposed a probabilistic non-parametric method using kernel density estimation. [PERSON] et al. [1] introduced a sample-based method in background modeling. Samples from previous predefined frames are used in background modeling. If there is a predefined group of samples that is close to the current pixel, then it is considered as background, otherwise, it is considered as foreground. [PERSON] et al. [12] proposed a method that uses a codebook. At the initial stage, codewords from intensity, color, and temporal features are constructed. They build up a codebook for later segmentation. The current frame's pixel values of intensity and color are compared to those of the codewords in the code book. Finally, a foreground or background label is assigned to each pixel by comparing the distance with codewords in the codebook. In the case of a background pixel, the matching codeword is updated. [PERSON] et al. [13] proposed a method based on principal component analysis, which is called the eigenbackground. The mean and the covariance matrix are computed using a predefined number of images. Here, N eigenvectors are chosen corresponding to the N largest eigenvalues, and they are used as the background model. Incoming images are projected into those eigenvectors, and their distance in those spaces is used to identify the foreground and background. [PERSON] et al. [14] proposed a method that uses a Gaussian mixture model for the background and uses single Gaussian for the foreground. They employed a flux tensor [15] that can explain variations of optic flow within a local 3D spatio-temporal volume, and it is used in detecting blob motion. With information from blob motion, foreground and background models are integrated to find moving and static foreground objects. Additionally, edge matching [16] is used to classify static foreground objects as ghosts or intermediate motions. [PERSON] et al. [3] proposed a method that applies a region-based mixture of Gaussians for foreground object segmentation to cope with the sensitivity of the dynamic background. Additionally, [PERSON] et al. [17] proposed an algorithm that uses a mixture of Gaussians in a local region. At each pixel level, the foreground and background are modeled using a mixture of Gaussians. Each pixel is determined to be foreground or background by finding the highest probability of the center pixel around an N \(\times\) N region. [PERSON] and [PERSON] [18] proposed an algorithm to cope with sudden illumination changes by using multiple background models through single Gaussians and different color representations. K-means clustering is used to classify the pixels of input images. For each pixel, K models are compared, and the group that shows the highest normalized cross-correlation is chosen. An RGB and YCbCr color frame is used, and segmentation is done for each color, which yields six segmentation masks. Finally, background segmentation is performed by integrating all available segmentation masks. [PERSON] et al. [19] proposed an algorithm that improves [PERSON] et al. [1]. They replace the global threshold R with an adaptive threshold R(x) that depends on the pixel location and a metric of the background model which is called background dynamics. The threshold R(x) and the model update rate are determined by a feedback loop using the additional information from the background dynamics.