Automated human fall recognition from visual data

Falls are one of the greatest risks for the older adults living alone at home. This research presents a novel visual-based fall detection approach to support independent living for older adults in an indoor environment. The aim of the research was to investigate appropriate methods for detecting falls through analysing the motion and shape of the human body. Several techniques for automatically detecting falls have been proposed. The existing technologies can be classified into three main groups of fall detectors, namely: ambient device-based, wearable sensor-based and computer vision-based techniques. Ambient device-based techniques use vibration or pressure sensors to capture the sound and vibration for detecting the presence and position of a person. Although these devices are inexpensive and do not disturb the user, the detection rate is rather low and many false alarms are generated. Wearable devices use different sensors such as accelerometer and gyroscopes to capture the human body movement information and detect falls. However, older adults often forget to wear them. Wearable sensors are also known to be too invasive as they require wearing and carrying various uncomfortable devices. Much work has been undertaken to investigate the use of visual-based sensors for fall detection using single, multiple, and omnidirectional cameras. The proposed research reported in this thesis uses a single camera to detect a moving object using a background subtraction algorithm. The next step is to extract robust features which describe the change in human shape and to discriminate falls from other activities like lying and sitting. These features are based on motion, change in the human shape feature, projection histogram features and temporal change of head position. Features extracted from the human silhouette are finally fed into various machine learning classifiers for fall detection evaluation. The ability to distinguish a fall action depends mainly on the quality of the classifier inputs, therefore, the features of the extracted human silhouette play a key role in the effectiveness and robustness of detecting human falls. In this research, the timed Motion History Image (tMHI) method is applied for motion segmentation. In addition, the motion information was combined with other features extracted from the fitted ellipse around the human body to discriminate actual fall from other activities. Fall detection methods can be divided into two main categories; thresh- old based methods and machine learning-based methods. This research presents threshold-based methods to distinguish between Activities of Daily Living (ADL) and falls. Fall events can be detected if the measured features values higher than pre-determined threshold values. Results show that falls can be distinguished from ADL with an accuracy of 99:82%, using our recording dataset. In addition, various machine learning methods were compared to evaluate their abilities to accurately detecting falls. Experimental results show efficiency and reliability of the proposed fall detection approach with high fall detection rate of 99:60% and low false alarm 2:62% tested with UR Fall Detection dataset. Additionally, A set of experiments have been conducted using our recording dataset, the results indicate that the proposed approach achieves high fall detection rate 99:94% and low false alarm 0:02%

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Automated detection of block falls in the north polar region of Mars

We developed a change detection method for the identification of ice block falls using NASA's HiRISE images of the north polar scarps on Mars. Our method is based on a Support Vector Machine (SVM), trained using Histograms of Oriented Gradients (HOG), and on blob detection. The SVM detects potential new blocks between a set of images; the blob detection, then, confirms the identification of a block inside the area indicated by the SVM and derives the shape of the block. The results from the automatic analysis were compared with block statistics from visual inspection. We tested our method in 6 areas consisting of 1000x1000 pixels, where several hundreds of blocks were identified. The results for the given test areas produced a true positive rate of ~75% for blocks with sizes larger than 0.7 m (i.e., approx. 3 times the available ground pixel size) and a false discovery rate of ~8.5%. Using blob detection we also recover the size of each block within 3 pixels of their actual size

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms