Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Occlusion <s> Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Occlusion <s> The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. This paper describes system for monitoring and fall detection of ELDERLY PEOPLE using triaxial accelerometer together with ZigBee transceiver to detect fall of ELDERLY PEOPLE. The Accidental Fall Detection System will be able to assist careers as well as the elderly, as the careers will be notified immediately to the intended person. This fall detection system is designed to detect the accidental fall of the elderly and alert the careers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the receiver <s> BIB002 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Occlusion <s> We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios. <s> BIB003
Line of sight obstructions are a major problem in camera-based systems. If the subject is being blocked by an object, no analysis of his/her current activity can be performed. In fact, the entire system becomes useless at that point. The work in BIB002 suffers from occlusion since subjects can sometimes be behind a sofa or furniture while been monitored. The most common solution is to place multiple cameras in different areas of interest to widen the visibility range so the target is visible from any angle. The drawback of this solution is the increase in computing complexity and cost. Depth cameras are used to mitigate occlusion problems. These cameras make use of two or more sensors to generate 3D depth perception. Images are created using range imaging techniques. Some of these techniques are BIB001 : stereo triangulation, sheet of light triangulation, structured light, and time of flight. Stereo triangulation is achieved by finding the corresponding points in two frames from two different cameras. In sheet of light triangulation, a source of light is projected into the scene creating a reflected line as seen from the source; the shape of the reflected line is used to measure the distance of the light source and the reflected points. Structured light works similar to sheet of light triangulation but the scene is illuminated with a predefined light pattern. Time of flight is similar to a radar, but it uses a light pulse instead of an RF pulse; it measures the distance of the objects in the scene based on the speed of light. A typical example of a depth camera is the popular Microsoft Kinect. It uses two sensors (an infrared projector and an infrared sensor) to generate a 3D map of the scene BIB003 . The use of a 3D map allows to differentiate between obstructions and the subject (depending on the camera position); therefore, further analysis can be done on the current activity of the subject.