## 1 Introduction  
In recent years, 24/7 monitoring systems for dependent persons who are living alone at home have become an important topic of research in the field of image technology. Here, the term dependent persons not only includes older adults who require care with regular, long-term monitoring, but also disabled persons, and patients with chronic diseases [1]. Such people may have problems with mobility that can affect their health, quality of life, and expected life span. Falls are the most common injuries facing dependent persons. Accordingly, much research has focused on fall detection. Most systems in use for detecting abnormal events or falls are classified into three groups: those using wearable-sensors [2],or ambient sensors [3], and those using computer-vision and image processing [4; 5; 6]. Wearable sensors are commonly used to collect information related to body movements and provide notification when a fall occurs. However, movement-based systems cannot provide notification if the person is already unconscious after falling. Ambient sensors can be installed under the bed or floor to capture the vibration that occurs when a person falls. Although such sensors do not disturb the person, they can generate false alarms. For these reasons, a system based on computer vision is more beneficial and reliable. In addition, visual surveillance systems can detect specific human activities, such as walking, sitting, and lying down [7; 8].  
Therefore, we propose a vision-based system for home care monitoring that detects normal as well as abnormal events, including falls. The main contributions of this paper are described in the following developments.  
* A detection system for visible abnormal and normal events based on data gathered using an RGB video camera;
* A modified method of statistical analysis involving virtual grounding point features that provides reliable information, not only on the exact time of a fall, but also on the pre-impact period of a fall.  
In this study, our proposed approach makes an effort to improve the detection rates for abnormal and normal events in a home-care monitoring system. Since our intention is to develop a long-term monitoring system for an assisted-living environment, we take great care in considering the extracted features for the details of human posture for precisely detecting falls. In this study, we consider new features using the concept of a virtual grounding point for the human body, and related visual features. We conduct abnormal event analysis including modified statistical analysis. Finally, the decision-making process is performed by applying a support vector machine, and a new consideration that involves detecting the period of a fall.  
The following provides a step-by-step description of system and method. We firstly conduct foreground and background separation to detect both moving and motionless people in the video scenes. Secondly, we perform feature extraction, including the construction of a virtual grounding point and its associated visual features. Thirdly, we obtain analytical information for fall events by analyzing moving averages for the extracted features, and computing differences for the observed moving averages for the extracted features. Finally, a support vector machine is used to set the rules for decision making and detect the period of the event in effectively distinguishing abnormal and normal events. The rest of this paper is organized as follows: Section 2 presents related works; Section 3 presents the methodologies of our proposed system; Section 4 presents and evaluates experimental results, and finally, Section 5 presents the conclusion and speculates on future trends in our approach.