Moving object detection via TV-L1 optical flow in fall-down videos

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524

Towards Unsupervised Sudden Group Movement Discovery for Video Surveillance

International audienceThis paper presents a novel and unsupervised approach for discovering "sudden" movements in video surveillance videos. The proposed approach automatically detects quick motions in a video, corresponding to any action. A set of possible actions is not required and the proposed method successfully detects potentially alarm-raising actions without training or camera calibration. Moreover, the system uses a group detection and event recognition framework to relate detected sudden movements and groups of people, and provide a semantical interpretation of the scene. We have tested our approach on a dataset of nearly 8 hours of videos recorded from two cameras in the Parisian subway for a European Project. For evaluation, we annotated 1 hour of sequences containing 50 sudden movements

Central monitoring system for ambient assisted living

Smart homes for aged care enable the elderly to stay in their own homes longer. By means of various types of ambient and wearable sensors information is gathered on people living in smart homes for aged care. This information is then processed to determine the activities of daily living (ADL) and provide vital information to carers. Many examples of smart homes for aged care can be found in literature, however, little or no evidence can be found with respect to interoperability of various sensors and devices along with associated functions. One key element with respect to interoperability is the central monitoring system in a smart home. This thesis analyses and presents key functions and requirements of a central monitoring system. The outcomes of this thesis may benefit developers of smart homes for aged care

Two Dimensional (2D) Visual Tracking in Construction Scenarios

The tracking of construction resources (e.g. workforce and equipment) in videos, i.e., two-dimensional (2D) visual tracking, has gained significant interests in the construction industries. There exist lots of research studies that relied on 2D visual tracking methods to support the surveillance of construction productivity, safety, and project progress. However, few efforts have been put on evaluating the accuracy and robustness of these tracking methods in the construction scenarios. Meanwhile, it is noticed that state-of-art tracking methods have not shown reliable performance in tracking articulated equipment, such as excavators, backhoes, and dozers etc. The main objective of this research is to fill these knowledge gaps. First, a total of fifth (15) 2D visual tracking methods were selected here due to their excellent performances identified in the computer vision field. Then, the methods were tested with twenty (20) videos captured from multiple construction job sites at day and night. The videos contain construction resources, including but not limited to excavators, backhoes, and compactors. Also, they were characterized by the attributes, such as occlusions, scale variation, and background clutter, in order to provide a comprehensive evaluation. The tracking results were evaluated with the sequence overlap score, center error ratio, and tracking length ratio respectively. According to the quantitative comparison of tracking methods, two improvements were further conducted. One is to fuse the tracking results of individual tracking methods based on the non-maximum suppression. The other is to track the articulated equipment by proposing the idea of tracking the equipment parts respectively. The test results from this research study indicated that 1) the methods built on the local sparse representation were more effective; 2) the generative tracking strategy typically outperformed the discriminative one, when being adopted to track the equipment and workforce in the construction scenarios; 3) the fusion of the results from different tracking methods increased the tracking performance by 10% in accuracy; and 4) the part-based tracking methods improved the tracking performance in both accuracy and robustness, when being used to track the articulated equipment

Intelligent computer vision processing techniques for fall detection in enclosed environments

Detecting unusual movement (falls) for elderly people in enclosed environments is receiving increasing attention and is likely to have massive potential social and economic impact. In this thesis, new intelligent computer vision processing based techniques are proposed to detect falls in indoor environments for senior citizens living independently, such as in intelligent homes. Different types of features extracted from video-camera recordings are exploited together with both background subtraction analysis and machine learning techniques. Initially, an improved background subtraction method is used to extract the region of a person in the recording of a room environment. A selective updating technique is introduced for adapting the change of the background model to ensure that the human body region will not be absorbed into the background model when it is static for prolonged periods of time. Since two-dimensional features can generate false alarms and are not invariant to different directions, more robust three-dimensional features are next extracted from a three-dimensional person representation formed from video-camera measurements of multiple calibrated video-cameras. The extracted three-dimensional features are applied to construct a single Gaussian model using the maximum likelihood technique. This can be used to distinguish falls from non-fall activity by comparing the model output with a single. In the final works, new fall detection schemes which use only one uncalibrated video-camera are tested in a real elderly person s home environment. These approaches are based on two-dimensional features which describe different human body posture. The extracted features are applied to construct a supervised method for posture classification for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build a robust fall detection model

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000

Towards a Single Sensor Passive Solution for Automated Fall Detection

Abstract—Falling in the home is one of the major challenges to independent living among older adults. The associated costs, coupled with a rapidly growing elderly population, are placing a burden on healthcare systems worldwide that will swiftly become unbearable. To facilitate expeditious emergency care, we have developed an artificially intelligent camera-based system that automatically detects if a person within the field-of-view has fallen. The system addresses concerns raised in earlier work and the requirements of a widely deployable in-home solution. The presented prototype utilizes a consumer-grade camera modified with a wide-angle lens. Machine learning techniques applied to carefully engineered features allow the system to classify falls at high accuracy while maintaining invariance to lighting, environment and the presence of multiple moving objects. This paper describes the system, outlines the algorithms used and presents empirical validation of its effectiveness. I

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000