Fall detection using Gaussian mixture model and principle component analysis

Poonsri, Arisa; Chiracharit, Werapon

doi:10.1109/iciteed.2017.8250441

Cited by 30 publications

(15 citation statements)

References 12 publications

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“…Only when the ideal human body contour is extracted, the falling behavior can be correctly classified according to the foreground contour. In the task of fall detection, the most commonly used algorithms are Frame difference [21], GMM [22], and GMG [23], but these methods are sensitive to light, shadow, and ghosting of moving objects.…”

Section: A Mask-rcnn For Background Subtractionmentioning

confidence: 99%

“…Meanwhile, many deep learning based background subtraction works [18][19][20] have been introduced in recent years. The former including Frame difference, Gaussian Mixture Model (GMM), Geometric Multigrid (GMG) [21][22][23], Fuzzy methods [24][25][26] and RPCA methods [27,28], whereas the latter one commonly employs Yolov3 and Faster R-CNN [29][30][31]. However, the conventional technique does not perform well when the lighting changes, shadow changes, and the changes in the background due to short-term movements, which is difficult to meet the urgent needs of fall detection in complicated scenes at present.…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Chen

Wang

et al. 2020

IEEE Access

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Fall event, as one of the greatest risks to the elderly, its detection has been a hot research issue in the solitary scene in recent years. Nevertheless, most current researches are conducted in the ideal environments, without considering the challenge of complex background in real situation. Therefore, this paper aims to detect fall event detection in complex background based on visual data. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method is first used to accurately extract the moving objects in the noise background. Then, an attention guided Bi-directional LSTM model is proposed for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other stateof-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.

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Section: A Mask-rcnn For Background Subtractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Chen

Wang

et al. 2020

IEEE Access

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“…A fully connected neural network is then used to generate fall/no fall signal [14]. Poonsri and Chiracharit have used Gaussian mixture models, and principal component analysis (PCA) to extract features such as orientation of human silhouettes in a video and aspect ratio for detecting falls [15]. Zerrouki et al extracted silhouette‐based features.…”

Section: Related Workmentioning

confidence: 99%

Human activity recognition using 2D skeleton data and supervised machine learning

et al. 2019

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Vision‐based human activity recognition (HAR) finds its application in many fields such as video surveillance, robot navigation, telecare and ambient intelligence. Most of the latest researches in the field of automated HAR based on skeleton data use depth devices such as Kinect to obtain three‐dimensional (3D) skeleton information directly from the camera. Although these researches achieve high accuracy but are strictly device dependent and cannot be used for videos other than from specific cameras. Current work focuses on the use of only 2D skeletal data, extracted from videos obtained through any standard camera, for activity recognition. Appearance and motion features were extracted using 2D positions of human skeletal joints through OpenPose library. The approach was trained and tested on publically available datasets. Supervised machine learning was implemented for recognising four activity classes including sit, stand, walk and fall. Performance of five techniques including K‐nearest neighbours (KNNs), support vector machine, Naive Bayes, linear discriminant and feed‐forward back‐propagation neural network was compared to find the best classifier for the proposed method. All techniques performed well with best results obtained through the KNN classifier.

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“…Computer-vision approaches monitor an imaged subject by using cameras [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. They analyze the change of body shape by computing different features, such as the ratio between height and width of the box surrounding the person, the histogram projection of the silhouette, the coordinates of an ellipse surrounding the person and the key joints of the person’s skeleton.…”

Section: Introductionmentioning

confidence: 99%

Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices

et al. 2021

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Falls are one of the most critical health care risks for elderly people, being, in some adverse circumstances, an indirect cause of death. Furthermore, demographic forecasts for the future show a growing elderly population worldwide. In this context, models for automatic fall detection and prediction are of paramount relevance, especially AI applications that use ambient, sensors or computer vision. In this paper, we present an approach for fall detection using computer vision techniques. Video sequences of a person in a closed environment are used as inputs to our algorithm. In our approach, we first apply the V2V-PoseNet model to detect 2D body skeleton in every frame. Specifically, our approach involves four steps: (1) the body skeleton is detected by V2V-PoseNet in each frame; (2) joints of skeleton are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank 2 to build time-parameterized trajectories; (3) a temporal warping is performed on the trajectories, providing a (dis-)similarity measure between them; (4) finally, a pairwise proximity function SVM is used to classify them into fall or non-fall, incorporating the (dis-)similarity measure into the kernel function. We evaluated our approach on two publicly available datasets URFD and Charfi. The results of the proposed approach are competitive with respect to state-of-the-art methods, while only involving 2D body skeletons.

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Fall detection using Gaussian mixture model and principle component analysis

Cited by 30 publications

References 12 publications

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Human activity recognition using 2D skeleton data and supervised machine learning

Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices

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