Spatio-temporal fall event detection in complex scenes using attention guided LSTM

Feng, Qianjin; Gao, Chenqiang; Wang, Lan; Zhao, Yue; Song, Tianyou; Li, Qiang

doi:10.1016/j.patrec.2018.08.031

Cited by 94 publications

(47 citation statements)

References 23 publications

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“…To further locate the region of interest in each frame, a LSTM (Long Short-Term Memory) based spatial visual attention scheme is incorporated. In the same way, [24] propose an attention guided LSTM model fall events classification. We can conclude that the motion and shape information are less exploited with current state-of-the-art fall recognition methods based on deep learning despite its significant contribution in fall recognizing as a handcrafted feature.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Based on Dynamic Motion and Shape Variations for Elderly Fall Detection

Khraief¹,

Benzarti²,

Amiri³

2019

IJMLC

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Fall detection became a major concern especially for elderly who lives alone at home. Unexpected situations might happen that influence their health, security and well-being. The development of an intelligent surveillance system is required to alleviate the negative effects of unforeseen circumstances and assisting the elderly in independent living. Currently, convolutional neural network has been successfully used for solving various computer vision tasks, such as object detection and recognition. In this paper, we propose a new vision system for elderly fall detection based on new two stream convolutional neural networks. First, human silhouette is extracted based on background subtraction and person recognition. Second, history of binary motion image HBMI is fed into the first stream characterizing the human shape variations. The second stream is based on amplitude and orientation of optical flow defining the velocity and the direction of the human motion. The system classifies fall events using score fusion schema. Transfer learning is performed to deal with the small amount of fall datasets. Our final network outperforms state-of-the-art results on standard fall datasets.

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Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Based on Dynamic Motion and Shape Variations for Elderly Fall Detection

Khraief¹,

Benzarti²,

Amiri³

2019

IJMLC

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“…The deep learning method [30,53,54] has been widely used in computer vision in recent years. Unlike the most conventional vision-based fall detection methods [55] relying on hand-crafted features, the methods based on deep learning techniques can automatically learn features and hence have got widely concerned recently.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…But, Bi-directional LSTM is the most performant to other classifiers [54]. In 2020, VGG-16 net combined with an attention guided LSTM was applied to capture spatial-temporal features for fall detection [30]. In 2020, an extremely deep residual network and LSTM network were used for fall detection [53].…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…To solve the above problem, in this paper, we propose a Mask R-CNN with attention guided Bi-directional LSTM fall detection method to handle the complex background environment, which integrates the information of spatial and temporal domains in complex scenes. We show that using our model for fall detection gives better results compared to the existing models [30,[34][35][36][37]. In our method, an effective person detector Mask R-CNN is used for background subtraction firstly, and then the Convolutional neural network is utilized for feature extraction.…”

Section: Introductionmentioning

confidence: 96%

“…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%

See 2 more Smart Citations

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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Contactless Fall Detection for the Elderly

Nahian

Raju

Tasnim

et al. 2021

Contactless Human Activity Analysis

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Spatio-temporal fall event detection in complex scenes using attention guided LSTM

Cited by 94 publications

References 23 publications

Convolutional Neural Network Based on Dynamic Motion and Shape Variations for Elderly Fall Detection

Convolutional Neural Network Based on Dynamic Motion and Shape Variations for Elderly Fall Detection

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

Contactless Fall Detection for the Elderly

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