# An Image-Based Fall Detection System for the Elderly
## 2 Related Works
### Image-Based Methods  
Most of the image-based methods can be categorized into four types: multi-camera, single-camera, depth-camera, and wearable-camera methods. For multi-camera methods, [PERSON] et al. [5] reconstructed 3D shapes of people using multiple cameras and detected falls by analyzing the volume distribution along the vertical axis. However, it is hard and impractical to track people using multiple cameras in public areas.  
For single-camera methods, [PERSON] et al. [7] proposed a posture recognition method based on fuzzy logic. In order to detect a person among other moving objects, they adopted a machine learning technology to recognize a person [8]. [PERSON] et al. [26] likewise proposed a posture recognition method by analyzing the human silhouette. In order to identify a human body among moving objects, the object with the greatest number of moving pixels is regarded as a human body. Then, ellipse fitting and a projection histogram are used as the global and local features, respectively, to describe different postures. Finally, a directed acyclic graph support vector machine (DAGSVM) is used to classify the posture. However, if the person lies on the ground, the method may issue a false alarm. [PERSON] et al. [27] used two different background separation methods to find a human silhouette and used the area of the silhouette as a feature to feed into a support vector machine (SVM) for classification. [PERSON] et al. [28] used background subtraction to find objects in the foreground and categorized a human by contour-based human template matching. They detected a fall by computing the distance between the top and mid center of the bounding box of a human. [PERSON] et al. [29] adopted a background subtraction and Gaussian mixture model to detect human objects. They then computed the orientation, aspect ratio, and area ratio to extract features and classify the postures. However, background subtraction may not be able to correctly detect multiple human objects and results in an inaccurate classification result. Furthermore, if the person is obscured by other objects, the above mentioned methods may not work properly.  
For depth-camera methods, [PERSON] et al. [30] proposed a method that involves extracting curvature scale space (CSS) features of human silhouettes from each frame and representing the action by a bag of CSS (BoCSS) words. They then identify the BoCSS representation of a fall from those of other actions by utilizing the extreme learning machine (ELM) classifier. [PERSON] et al. [31] proposed another method to detect the motion of a fall by using the SVM classifier with the input of the 3D trajectory of the head joint. [PERSON] et al. [6] evaluated the distance of a 3D human centroid from the floor plane to detect falls. [PERSON] et al. [32] used the Microsoft Kinect sensor to detect a fall by collecting information on the velocity, acceleration, and width height ratio of a human object. However, the above-mentioned methods may not be accurate when the person lies on the ground. In addition, these methods cannot recognize the difference between falls and sitting on the ground. In summary, all the above-mentioned methods focus on falls that occur during forward walking. They cannot accurately detect falls that occur while sitting down and standing up from a chair.  
For wearable-camera methods, [PERSON] et al. [33] used a reverse approach by taking a completely different view compared with the existing vision-based systems. The system employed a modified version of the histograms of oriented gradients (HOG) approach together with gradient local binary patterns (GLBP), and it detected falls by the change in the dissimilarity distances obtained by HOG and GLBP. However, the wearable-camera methods are not practical for daily usage, because the elderly usually suffer from forgetfulness.