## 1 Introduction  
With the continuous aging of the global population, there has been an alarming increase in the number of incidents of the elderly falling that cause severe injuries and sometimes even death. According to the World Health Organization in [1], 28-35% of people aged 65 or more encounter falls and the incident rate is even higher for people aged 70 or above. Falls can be potentially fatal in cases when an elder is alone. This is resulting in a steady increase in the social costs involving pre-incident preventive measures and post-incident treatments. Hence, multiple recent studies that have been undertaken aim to develop an intelligent solution that automatically detects falls and raises an emergency alert targeted to an elderly person who lives alone. However, no perfect solution has been developed to date due to challenges, such as non-deterministic fall patterns and the illumination factor. The most recent fall detection studies are based on wearable, ambience, or vision sensors [2]. The wearable device-based fall detection system uses accelerometers, gyroscopes, or other body sensors embedded in garments [3; 4; 5; 6; 7]. The wearable sensor-based fall detection methods uncover changes in a human body through wearable sensors attached to the body. This method is simple, computationally efficient, and easy to install. However, as several kinds of sensors need to be physically attached to the body, such a wearable sensor-based fall detection system proves to be slightly inconvenient to the users. In addition, it needs periodic replacement of batteries that power it and may have side effects due to a lasting exposure to electromagnetic waves. Although the accuracy of alarms can be improved by fusing different sensors [8; 9], such a conjugated method often leads to inconvenience. An ambience-based fall detection system typically uses vibration sensors, infrared (IR) sensors, pressure sensors, and several other devices that can be used individually or in combination to detect the position and abnormality of a person to trigger a fall alarm [10; 11; 12; 13; 14]. Unlike a wearable sensor-based fall detection method, the ambience-based fall detection system is a non-invasive method, however there are many false alarms. Moreover, it is difficult to use this system comprehensively in indoor or outdoor environments because a fall can only be detected in an area that is equipped with sensors. With new advancements in computer vision research, diverse vision-based fall detection systems are being studied. Such vision-based fall detection systems can be classified into three main categories: single camera-based fall detection systems, multi-camera-based fall detection systems, and depth camera-based fall detection systems. In general, numerous single camera-based fall detection systems extract an individual's silhouette by adopting a background model (e.g., a background reference image). From the individual silhouette, various features such as the width and the length of a bounding box, the ellipse aspect ratio of the orientation, and edges are used to detect a fall [15; 16; 17]. However, the constantly and dynamically changing background of recorded scenes due to illumination and the presence of shadows in outdoor environments causes a lot of errors. Moreover, the aspect ratio of a bounding box or an ellipse, commonly used for human tracking with 2D images, is not sufficient to detect a fall accurately. More specifically, some studies [18; 19] related to single camera cases tried to detect falls based on the motion contour by analyzing the motion energy or using the integrated spatiotemporal energy map. However, the fact that the analysis of motion energy varies from person to person makes it difficult to be used for general cases. In addition, a single camera is not capable of providing 3D information required for a robust fall detection.