## 2 Related Works  
In most video monitoring systems, the most fundamental step is background subtraction, which assumes that the distribution of background pixels can be separated from that of foreground pixels in detecting a silhouetted object. Methods used for this purpose involve statistical measures such as median and mean [9] to model the background. In addition, a more complicated distribution for background pixels can be obtained using models such as mixture of Gaussians (_MoG_) [10] and mixture of generalized Gaussians (_MoGG_) [11]. As these methods do not always provide great performance due to not taking a knowledge of video structure into consideration, a low-rank subspace learning approach has been proposed [12] to take account of the video structure, including the temporal similarity of the background scene, and the spatial contiguity of foreground objects. However, most of these methods focus on solving for detection of moving objects in video scenes. When the foreground objects move very slowly, the redundant data occurs, resulting in serious outliers. To solve this problem, methods are applied for motion detection and frame differencing, eliminating redundant data [13]. However, this technique can result in a loss of useful information in real-life video sequences. Therefore, we have applied a graph cut theory as a solution for refining the results of background subtraction [14; 15]. In this study, we achieved foreground refinement by combining _MoG_ with the low-rank subspace learning method [12] for background subtraction and the graph cut algorithm [15]. In order to represent human objects in video sequences, shape analysis is performed using the bounding box method, which gives the attributes of height and width to calculate the aspect ratio in defining a fall. Our previous method for action analysis first involved creating a fitted ellipse bounding box around the object body [16]. The moment is then computed for the continuous images and the ellipse's center. Its major axis, minor axis, and its orientation are used as the observed features for human actions. In addition, horizontal and vertical histograms are constructed to obtain good performance for posture detection of the object. An additional method in our previous work for feature extraction involved the variation of motion using timed motion history images (_tMHI_). This had provided the basic facts for detecting great motion in abnormal scenes. However, these techniques required fixed threshold values for analyzing and detecting the events. In addition, the movement characteristics in the walking patterns of the human body are studied by determining the features of temporal variability, such as joint angle (ankle, knee, hip, torso) movements. However, additional observations are needed in which values for temporal variations with the pre-selected variables might deviate during investigation, and useful information for analysis on potential features might be discarded. In this regard, the proposed method [17] uses movement variability throughout the whole body, which need not consider pre-selected variables. However, this system uses a full-body marker set, which consists of 28 markers placed on human body segments for gait analysis. Requiring the elderly and dependent persons who have chronic diseases to wear markers 24 h per day is unrealistic. Moreover, we propose a three-dimensional model of the human body [18] based on the visual appearance of the human subject that changes over time in aspects such as self-shadowing, and clothing deformation. The idea is to develop an adaptive, appearance model for articulated body parts by using a mixture model that includes an adaptive template and a frame-to-frame matching process. As motivation for this approach, research featuring background subtraction from foreground silhouettes has not provided reliable tracking. This suggests that a three-dimensional articulated model of the human body must be developed without using a high-quality silhouette provided by background subtraction.