A. St-Arnaud

Bio: A. St-Arnaud is an academic researcher. The author has contributed to research in topics: Object detection & Population. The author has an hindex of 1, co-authored 1 publications receiving 322 citations.

Fall Detection from Human Shape and Motion History Using Video Surveillance

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.

Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

Abstract: Faced with the growing population of seniors, developed countries need to establish new healthcare systems to ensure the safety of elderly people at home. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, a new method is proposed to detect falls by analyzing human shape deformation during a video sequence. A shape matching technique is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally, falls are detected from normal activities using a Gaussian mixture model. This paper has been conducted on a realistic data set of daily activities and simulated falls, and gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.

Video-Based Abnormal Human Behavior Recognition—A Review

Abstract: Modeling human behaviors and activity patterns for recognition or detection of special event has attracted significant research interest in recent years. Diverse methods that are abound for building intelligent vision systems aimed at scene understanding and making correct semantic inference from the observed dynamics of moving targets. Most applications are in surveillance, video content retrieval, and human-computer interfaces. This paper presents not only an update extending previous related surveys, but also a focus on contextual abnormal human behavior detection especially in video surveillance applications. The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention.

Approaches and principles of fall detection for elderly and patient

Abstract: Fall detection for elderly and patient has been an active research topic due to that the healthcare industry has a big demand for products and technology of fall detection. This paper gives a survey of fall detection for elderly and patient, focusing on identifying approaches and principles of the existing fall detection methods. To properly build the classification tree of the methods of fall detection we first study the characteristics of fall. Then according to what sensors and how sensors are used we first divide the methods of fall detection into three approaches: wearable device, ambience device, and camera-based. Further we divide each approach into two to three classes according to the used principles. For each class of algorithm we analyze their merits and demerits. We also give comments on how we can improve some algorithms.

A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

Abstract: We propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain postprocessing. Information from ellipse fitting and a projection histogram along the axes of the ellipse is used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.