An Intelligent Video Surveillance System for Anomaly Detection in Home Environment Using a Depth Camera
01 Jan 2019-pp 473-481
TL;DR: A simple yet efficient technique to detect fall with the help of inexpensive depth camera was presented and it was observed that SGD classifier gives better fall detection accuracy than the SVM classifier in both training and testing phase for SDU fall dataset.
Abstract: In recent years, the research on the anomaly detection has been rapidly increasing. The researchers were worked on different anomalies in videos. This work focuses on fall as an anomaly as it is an emerging research topic with application in elderly safety areas including home environment. The older population staying alone at home is prone to various accidental events including falls which may lead to multiple harmful consequences even death. Thus, it is imperative to develop a robust solution to avoid this problem. This can be done with the help of video surveillance along with computer vision. In this paper, a simple yet efficient technique to detect fall with the help of inexpensive depth camera was presented. Frame differencing method was applied for background subtraction. Various features including orientation angle, aspect ratio, silhouette features, and motion history image (MHI) were extracted for fall characterization. The training and testing were successfully implemented using SVM and SGD classifiers. It was observed that SGD classifier gives better fall detection accuracy than the SVM classifier in both training and testing phase for SDU fall dataset.
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TL;DR: A heterogeneous system that estimates the degree of an anomaly in unmanned surveillance drone by inspecting IMU (Inertial Measurement Unit) sensor data and real-time image in an unsupervised approach is demonstrated.
Abstract: Anomaly detection (AD) in a surveillance scenario is an emerging and challenging field of research. For autonomous vehicles like drones or cars, it is immensely important to distinguish between normal and abnormal states in real-time. Additionally, we also need to detect any device malfunction. But the nature and degree of abnormality may vary depending upon the actual environment and adversary. As a result, it is impractical to model all cases a-priori and use supervised methods to classify. Also, an autonomous vehicle provides various data types like images and other analog or digital sensor data, all of which can be useful in anomaly detection if leveraged fruitfully. To that effect, in this paper, a heterogeneous system is proposed which estimates the degree of abnormality of an unmanned surveillance drone, analyzing real-time image and IMU (Inertial Measurement Unit) sensor data in an unsupervised manner. Here, we have demonstrated a Convolutional Neural Network (CNN) architecture, named AngleNet to estimate the angle between a normal image and another image under consideration, which provides us with a measure of anomaly of the device. Moreover, the IMU data are used in autoencoder to predict abnormality. Finally, the results from these two algorithms are ensembled to estimate the final degree of abnormality. The proposed method performs satisfactorily on the IEEE SP Cup-2020 dataset with an accuracy of 97.3%. Additionally, we have also tested this approach on an in-house dataset to validate its robustness.
5 citations
01 Jan 2022
TL;DR: The smart jacket for visually impaired people or say visually impaired system (VIS) as discussed by the authors supports this process by providing key facilities a short-range system for detecting obstacles, a short range system for identifying obstacles, signboard recognition system, and the shortest path guidance system for source to destination.
Abstract: Blindness is one of those world's most feared afflictions. Blind people have trouble getting to the desired destination. The smart jacket for visually impaired people or say visually impaired system (VIS) supports this process by providing key facilities a short-range system for detecting obstacles, a short-range system for identifying obstacles, a signboard recognition system, and the shortest path guidance system for source to destination. Obstacle detection, distress calling, global location tracking, voice command functionality, and shortest route guidance are all features of this system in real time. The aim is to build a program that will direct visually disabled people arrive at the destination they want and help them understand the natural world around them. The blind or visually impaired rely primarily on other senses such as sound, touch, and smell to perceive their surroundings. We find it very daunting to go out alone, not to mention toilets, subway stations, restaurants, and so on. The visually impaired program seeks to make blind people fully exposed to their surroundings.
References
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TL;DR: A comprehensive survey of different systems for fall detection and their underlying algorithms is given, divided into three main categories: wearable device based, ambience device based and vision based.
Abstract: Fall detection is a major challenge in the public health care domain, especially for the elderly, and reliable surveillance is a necessity to mitigate the effects of falls. The technology and products related to fall detection have always been in high demand within the security and the health-care industries. An effective fall detection system is required to provide urgent support and to significantly reduce the medical care costs associated with falls. In this paper, we give a comprehensive survey of different systems for fall detection and their underlying algorithms. Fall detection approaches are divided into three main categories: wearable device based, ambience device based and vision based. These approaches are summarised and compared with each other and a conclusion is derived with some discussions on possible future work.
654 citations
TL;DR: The aim of this study was to provide a comprehensive review of health related smart home projects and discuss human factors and other challenges.
Abstract: Objectives A “smart home” is a residence wired with technology features that monitor the well-being and activities of their residents to improve overall quality of life, increase independence and prevent emergencies. This type of informatics applications targeting older adults, people with disabilities or the general population is increasingly becoming the focus of research worldwide. The aim of this study was to provide a comprehensive review of health related smart home projects and discuss human factors and other challenges. MethodsTo cover not only the medical but also the social sciences and electronics literature, we conducted extensive searches across disciplines (e.g., Medline , Embase , CINAHL, PsycINFO, Electronics and Communications Abstracts, Web of Science etc.). In order to be inclusive of all new initiatives and efforts in this area given the innovativeness of the concept, we manually searched for relevant references in the retrieved articles as well as published books on smart homes and gerontechnology Results A total of 114 publications (including papers, abstracts and web pages) were identified and reviewed to identify the overarching projects. Twenty one smart home projects were identified (71% of the projects include technologies for functional monitoring, 67% for safety monitoring, 47% for physiological monitoring, 43% for cognitive support or sensory aids, 19% for monitoring security and 19% to increase social interaction). Evidence for their impact on clinical outcomes is lacking. Conclusions The field of smart homes is a growing informatics domain. Several challenges including not only technical but also ethical ones need to be addressed.
342 citations
21 May 2007
TL;DR: A new method to detect falls, which are one of the greatest risk for seniors living alone, is proposed, based on a combination of motion history and human shape variation.
Abstract: Nowadays, Western countries have to face the growing population of seniors. New technologies can help people stay at home by providing a secure environment and improving their quality of life. The use of computer vision systems offers a new promising solution to analyze people behavior and detect some unusual events. In this paper, we propose a new method to detect falls, which are one of the greatest risk for seniors living alone. Our approach is based on a combination of motion history and human shape variation. Our algorithm provides promising results on video sequences of daily activities and simulated falls.
322 citations
TL;DR: A proof of concept to an automatic fall detection system for elderly people based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events.
Abstract: Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations. In this paper, we present a proof of concept to an automatic fall detection system for elderly people. The system is based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events. The classification is based on special features like shock response spectrum and mel frequency ceptral coefficients. For the simulation of human falls, we have used a human mimicking doll: ldquoRescue Randy.rdquo The proposed solution is unique, reliable, and does not require the person to wear anything. It is designed to detect fall events in critical cases in which the person is unconscious or in a stress condition. From the preliminary research, the proposed system can detect human mimicking dolls falls with a sensitivity of 97.5% and specificity of 98.6%.
304 citations
TL;DR: An overview of common ambulatory sensors is presented, followed by a summary of the developments in this field, with an emphasis on the clinical applications of falls detection, falls risk assessment, and energy expenditure.
Abstract: The rapid aging of the world's population, along with an increase in the prevalence of chronic illnesses and obesity, requires adaption and modification of current healthcare models. One such approach involves telehealth applications, many of which are based on sensor technologies for unobtrusive monitoring. Recent technological advances, in particular, involving microelectromechnical systems, have resulted in miniaturized wearable devices that can be used for a range of applications. One of the leading areas for utilization of body-fixed sensors is the monitoring of human movement. An overview of common ambulatory sensors is presented, followed by a summary of the developments in this field, with an emphasis on the clinical applications of falls detection, falls risk assessment, and energy expenditure. The importance of these applications is considerable in light of the global demographic trends and the resultant rise in the occurrence of injurious falls and the decrease of physical activity. The potential of using such monitors in an unsupervised manner for community-dwelling individuals is immense, but entails an array of challenges with regards to design c onsiderations, implementation protocols, and signal analysis processes. Some limitations of the research to date and suggestions for future research are also discussed.
241 citations