17,935 research outputs found
Comprehensive review of vision-based fall detection systems
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers
A Fallen Person Detector with a Privacy-Preserving Edge-AI Camera
As the population ages, Ambient-Assisted Living (AAL) environments are increasingly used to support older individuals’ safety and autonomy. In this study, we propose a low-cost, privacy-preserving sensor system integrated with mobile robots to enhance fall detection in AAL environments. We utilized the Luxonis OAKD Edge-AI camera mounted on a mobile robot to detect fallen individuals. The system was trained using YOLOv6 network on the E-FPDS dataset and optimized with a knowledge distillation approach onto the more compact YOLOv5 network, which was deployed on the camera. We evaluated the system’s performance using a custom dataset captured with a robot-mounted camera. We achieved a precision of 96.52%, a recall of 95.10%, and a recognition rate of 15 frames per second. The proposed system enhances the safety and autonomy of older individuals by enabling the rapid detection and response to falls.This work has been part supported by the visuAAL project on Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living (https://www.visuaal-itn.eu/) funded by the EU H2020 Marie Skłodowska-Curie grant agreement No. 861091. The project has also been part supported by the SFI Future Innovator Award SFI/21/FIP/DO/9955 project Smart Hangar
Computer vision based posture estimation and fall detection.
Falls are a major health problem, especially in the elderly population. Increasing fall events demands a high quality of service and dedicated medical treatment which is an economic burden. Serious injuries due to fall can cost lives in the absence of immediate care and support. There- fore, a monitoring system that can accurately detect fall events and generate instant alerts for immediate care is extremely necessary. To address this problem, this research aims to develop a computer vision-based fall detection system. This study proposes fall detection in three stages: (A) Detection of human silhouette and recognition of the pose, (B) Detection of the human as three regions for different postures including fall and (C) Recognise fall and non-fall using locations of human body regions as distinguishing features. The first stages of work comprise human silhouette detection and identification of activities in the form of different poses. Identifying a pose is important to understand a fall event where a change of pose defines its characteristics. A fall event comprises of sequential change of poses and ends up in a lying pose. Initial pose during a fall can be standing, sitting or bending but the final pose is usually a lying pose. It would, therefore, be beneficial if lying pose is recognised more accurately than other normal activities such as standing, sitting, bending or crawling to address a fall. Hence in the first stage, Background Subtraction (BS) is used to detect human silhouette. After background subtraction, the foreground images were used in a Convolutional Neural Network (CNN) to recognise different poses. The RGB and the Depth images were captured from a Kinect Sensor. The fusion of RGB and Depth images were explored for feeding to a convolutional neural net- work. Depth together with RGB complimented each other to overcome their weakness respectively and proved to be a significant strategy. The classification was performed using CNN to recognise different activities with 81% accuracy on validation. The other challenge in fall detection is the tracking of a person during a fall. Background Subtraction is not sufficient to track a fallen person especially when there are lighting and viewpoint variations in the environment and present of another object like furniture, a pet or even another person. Furthermore, tracking be- comes tougher during the fall in comparison to normal activities like walking or sitting because the rate of change pose is higher during a fall. To overcome this, the idea is to locate the regions in the body in every frame and consider it as a stable tracking strategy. The location of the body parts provides crucial information to distinguish falls from the other normal activities as the person is detected all the time during these activities. Hence the second stage of this research consists of posture detection using the pose estimation technique. This research proposes to use CNN based pose estimation using simplified human postures. The available joints are grouped according to three regions: Head, Torso and Leg and then finally fed to the CNN model with just three inputs instead of several available joints. This strategy added stability in pose detection and proved to be more effective against complex poses observed during a fall. To train the CNN model, transfer learning technique was used. The model was able to achieve 96.7% accuracy in detecting the three regions on different human postures on the publicly available dataset. A system which considers all the lying poses as falls can also generate a higher false alarm. Lying on bed or sofa can easily generate a fall alarm if they are recognised as falls. Hence, it is important to recognise actual fall by considering a sequence of frames that defines a fall and not just the lying pose. In the third and final stage, this study proposes Long Short-Term Memory (LSTM) recurrent networks-based fall detection. The proposed LSTM model uses the detected three region’s location as input features. LSTM is capable of using contextual information from the sequential input patterns. Therefore, the LSTM model was fed with location features of different postures in a sequence for training. The model was able to learn fall patterns and distinguish them from other activities with 88.33% accuracy. Furthermore, the precision of the fall class was 1.0. This is highly desirable in the case of fall detection as there is no false alarm and this means that the cost incurred in calling medical support for a false alarm can be completely avoided
Vision-based Human Fall Detection Systems using Deep Learning: A Review
Human fall is one of the very critical health issues, especially for elders
and disabled people living alone. The number of elder populations is increasing
steadily worldwide. Therefore, human fall detection is becoming an effective
technique for assistive living for those people. For assistive living, deep
learning and computer vision have been used largely. In this review article, we
discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based)
fall detection techniques. We also present a survey on fall detection benchmark
datasets. For a clear understanding, we briefly discuss different metrics which
are used to evaluate the performance of the fall detection systems. This
article also gives a future direction on vision-based human fall detection
techniques
Automatic Fall Risk Detection based on Imbalanced Data
In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine learning algorithms. After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research. The experiment shows that our approach is more interpretable with the key feature from skeleton information. Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Real-Time Online Human Tracking with a Stereo Camera for Person-Following Robots
Person-Following Robots have been studied for multiple decades now. Recently, person-following robots have relied on various sensors (e.g., radar, infrared, laser, ultrasonic, etc). However, these technologies lack the use of the most reliable information from visible colors (visible light cameras) for high-level perception; therefore, many of them are not stable when the robot is placed under complex environments (e.g., crowded scenes, occlusion, target disappearance, etc.). In this thesis, we are presenting three different approaches to track a human target for person-following robots in challenging situations (e.g., partial and full occlusions, appearance changes, pose changes, illumination changes, or distractor wearing the similar clothes, etc.) with a stereo depth camera. The newest tracker (SiamMDH, a Siamese convolutional neural network based tracker with temporary appearance model) implemented in this work achieves 98.92% accuracy with location error threshold 50 pixels and 92.94% success rate with IoU threshold 0.5 on our extensive person-following dataset
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