Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle
01 Dec 2018-Iet Computer Vision (The Institution of Engineering and Technology)-Vol. 12, Iss: 8, pp 1133-1140
TL;DR: A fall detection method for indoor environments based on the Kinect sensor and analysis of three-dimensional skeleton joints information is proposed and can be used in real-time video surveillance because of its time efficiency and robustness.
Abstract: Falls sustained by subjects can have severe consequences, especially for elderly persons living alone. A fall detection method for indoor environments based on the Kinect sensor and analysis of three-dimensional skeleton joints information is proposed. Compared with state-of-the-art methods, the authors' method provides two major improvements. First, possible fall activity is quantified and represented by a one-dimensional float array with only 32 items, followed by fall recognition using a support vector machine (SVM). Unlike typical deep learning methods, the input parameters of their method are dramatically reduced. Hence, videos are trained and recognised by an SVM with a low time cost. Second, the torso angle is imported to detect the start key frame of a possible fall, which is much more efficient than using a sliding window. Their approach is evaluated on the telecommunication systems team (TST) fall detection dataset v2. The results show that their approach achieves an accuracy of 92.05%, better than other typical methods. According to the characters of machine learning, when more samples are imported, their method is expected to achieve a higher accuracy and stronger capability of fall-like discrimination. It can be used in real-time video surveillance because of its time efficiency and robustness.
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TL;DR: The main focus of this survey is application of deep learning techniques in detecting the exact count, involved persons and the happened activity in a large crowd at all climate conditions.
Abstract: Big data applications are consuming most of the space in industry and research area. Among the widespread examples of big data, the role of video streams from CCTV cameras is equally important as other sources like social media data, sensor data, agriculture data, medical data and data evolved from space research. Surveillance videos have a major contribution in unstructured big data. CCTV cameras are implemented in all places where security having much importance. Manual surveillance seems tedious and time consuming. Security can be defined in different terms in different contexts like theft identification, violence detection, chances of explosion etc. In crowded public places the term security covers almost all type of abnormal events. Among them violence detection is difficult to handle since it involves group activity. The anomalous or abnormal activity analysis in a crowd video scene is very difficult due to several real world constraints. The paper includes a deep rooted survey which starts from object recognition, action recognition, crowd analysis and finally violence detection in a crowd environment. Majority of the papers reviewed in this survey are based on deep learning technique. Various deep learning methods are compared in terms of their algorithms and models. The main focus of this survey is application of deep learning techniques in detecting the exact count, involved persons and the happened activity in a large crowd at all climate conditions. Paper discusses the underlying deep learning implementation technology involved in various crowd video analysis methods. Real time processing, an important issue which is yet to be explored more in this field is also considered. Not many methods are there in handling all these issues simultaneously. The issues recognized in existing methods are identified and summarized. Also future direction is given to reduce the obstacles identified. The survey provides a bibliographic summary of papers from ScienceDirect, IEEE Xplore and ACM digital library.
219 citations
TL;DR: A global taxonomy for current fall-related studies from four aspects, including current literature reviews, fall detection, and prevention systems based on different sensor apparatus and analytic algorithm, low power techniques, and sensor placements for fall- related systems are conducted.
Abstract: Falls are abnormal activity events that occur infrequently; however, they are serious health problems among elderly individuals. With the advancements of technologies, falls have been widely studied by scientific researchers to minimize serious consequences and negative impacts. Fall detection and fall prevention are two strategies to tackle fall issues with a variety of sensing techniques and classifier models. Currently, many reviews on fall-related technologies have been presented and analyzed; however, most of them give surveys on the subfield of fall-related systems, while others are not extensive and comprehensive reviews. In fact, the latest researches have a new trend of fusion-based methods to improve the performance of the fall-related systems based on a combination of different sensors or classifier models. Adaptive threshold and radio frequency-based systems are also researched and proposed recently, which are seldom mentioned in other reviews. Therefore, a global taxonomy for current fall-related studies from four aspects, including current literature reviews, fall detection, and prevention systems based on different sensor apparatus and analytic algorithm, low power techniques, and sensor placements for fall-related systems are conducted in this paper. Several research challenges and issues in the fall-related field are also discussed and analyzed. The objective of this review paper is to conclude and provide a good position of current fall-related studies to inspire researchers in this field.
143 citations
23 Jun 2020
TL;DR: The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.
Abstract: Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.
114 citations
TL;DR: A comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made to determine the course of its evolution and help new researchers.
Abstract: 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
51 citations
Cites methods from "Support vector machine approach to ..."
...The double moving average filter used in [65] smooths vertical distance from joints to the ground plane....
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...The Kinect® system is also used in [65] to follow joints and estimate the vertical distance to the ground plane....
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...[65] 2018 Skeleton joint tracking model provided by MS Kinect® is used to estimate vertical/torso angle/depth characterization SVM Depth TST Fall Detection [37] Accuracy 92....
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TL;DR: The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory, therefore, it is suitable for mobile devices.
Abstract: The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices.
29 citations
Cites methods from "Support vector machine approach to ..."
...[20] proposed a fall detection method for indoor environments based on the Kinect sensor and analysis of three-dimensional skeleton joint information....
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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.
777 citations
TL;DR: A novel fall detection system based on the Kinect sensor that is capable of detecting walking falls accurately and robustly without taking into account any false positive activities (i.e. lying on the floor).
Abstract: This paper presents a novel fall detection system based on the Kinect sensor. The system runs in real-time and is capable of detecting walking falls accurately and robustly without taking into account any false positive activities (i.e. lying on the floor). Velocity and inactivity calculations are performed to decide whether a fall has occurred. The key novelty of our approach is measuring the velocity based on the contraction or expansion of the width, height and depth of the 3D bounding box. By explicitly using the 3D bounding box, our algorithm requires no pre-knowledge of the scene (i.e. floor), as the set of detected actions are adequate to complete the process of fall detection.
335 citations
TL;DR: An automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor in an “on-ceiling” configuration, and on the analysis of depth frames, which shows the effectiveness of the proposed solution, even in complex scenarios.
Abstract: We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.
190 citations
20 Jun 2011
TL;DR: An occlusion robust method is presented based on two features: human centroid height relative to the ground and body velocity, which is an efficient solution to detect falls as the vast majority of falls ends on the ground or near the ground.
Abstract: Falls are one of the major risks for seniors living alone at home. Computer vision systems, which do not require to wear sensors, offer a new and promising solution for fall detection. In this work, an occlusion robust method is presented based on two features: human centroid height relative to the ground and body velocity. Indeed, the first feature is an efficient solution to detect falls as the vast majority of falls ends on the ground or near the ground. However, this method can fail if the end of the fall is completely occluded behind furniture. Fortunately, these cases can be managed by using the 3D person velocity computed just before the occlusion.
170 citations
TL;DR: A behavior-based similarity measure is introduced that tells us whether two different space-time intensity patterns of two different video segments could have resulted from a similar underlying motion field, thus allowing to correlate dynamic behaviors and actions.
Abstract: We introduce a behavior-based similarity measure that tells us whether two different space-time intensity patterns of two different video segments could have resulted from a similar underlying motion field. This is done directly from the intensity information, without explicitly computing the underlying motions. Such a measure allows us to detect similarity between video segments of differently dressed people performing the same type of activity. It requires no foreground/background segmentation, no prior learning of activities, and no motion estimation or tracking. Using this behavior-based similarity measure, we extend the notion of two-dimensional image correlation into the three-dimensional space-time volume and thus allowing to correlate dynamic behaviors and actions. Small space-time video segments (small video clips) are "correlated" against the entire video sequences in all three dimensions (x, y, and t). Peak correlation values correspond to video locations with similar dynamic behaviors. Our approach can detect very complex behaviors in video sequences (for example, ballet movements, pool dives, and running water), even when multiple complex activities occur simultaneously within the field of view of the camera. We further show its robustness to small changes in scale and orientation of the correlated behavior.
170 citations