Human fall detection using slow feature analysis

Fan, Kaibo; Wang, Ping; Zhuang, Shuo

doi:10.1007/s11042-018-5638-9

Cited by 32 publications

(19 citation statements)

References 37 publications

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“…From Table 3 we can see that our TDRD gets the best result of average accuracy for six actions, sensitivity, and specificity on both depth-based methods and RGB-based methods. We get 96.04% average classification accuracy, which is about 14.71% higher than the best RGB-based method [31] and 1.04% higher than the best depth-based method [28]. We also get superior sensitivity and specificity.…”

Section: Comparison With Other Existing Methodsmentioning

confidence: 89%

Fall Detection in Videos With Trajectory-Weighted Deep-Convolutional Rank-Pooling Descriptor

Zhang

et al. 2019

IEEE Access

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Automatic fall detection in videos could enable timely delivery of medical service to the injured elders who have fallen and live alone. Deep ConvNets have been used to detect fall actions. However, there still remain problems in deep video representations for fall detection. First, video frames are directly inputted to deep ConvNets. The visual features of human actions may be interfered with surrounding environments. Second, redundant frames increase the difficulty of time encoding for human actions. To address these problems, this paper presents trajectory-weighted deep-convolutional rank-pooling descriptor (TDRD) for fall detection, which is robust to surrounding environments and can describe the dynamics of human actions in long time videos effectively. First, CNN feature map of each frame is extracted through a deep ConvNet. Then, we present a new kind of trajectory attention map which is built with improved dense trajectories to optimally localize the subject area. Next, the CNN feature map of each frame is weighted with its corresponding trajectory attention map to get trajectory-weighted convolutional visual feature of human region. Further, we propose a cluster pooling method to reduce the redundancy of the trajectory-weighted convolutional features of a video in the time sequence. Finally, rank pooling method is used to encode the dynamic of the cluster-pooled sequence to get our TDRD. With TDRD, we get superior result on SDUFall dataset and get comparable performances on UR dataset and Multiple cameras dataset with SVM classifiers. INDEX TERMS Fall detection, descriptor, deep ConvNets, trajectory attention map, cluster pooling, rank pooling.

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Section: Comparison With Other Existing Methodsmentioning

confidence: 89%

Fall Detection in Videos With Trajectory-Weighted Deep-Convolutional Rank-Pooling Descriptor

Zhang

et al. 2019

IEEE Access

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“…The proposed scheme consistently outperforms the methods in Refs. [19,27]. We also compare our method with the work in Ref.…”

Section: Comparison With Related Work and Discussionmentioning

confidence: 97%

“…Support vector machines (SVM), convolutional neural networks (CNN), extreme learning machines (ELM), Gaussian mixture models (GMM), naive Bayes (NB), and k-nearest neighbors (KNN) are the most common machine learning techniques used in Refs. [11,[14][15][16][19][20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

“…Many approaches have been proposed for fall detection based on analysing normal and abnormal activities with computer vision, and a few datasets are publicly available for testing. These approaches can be classified as thresholding based [12,13,18] or machine learning based [11,14,15,[19][20][21][22][23][24]. In this paper, we propose a new fall recognition system based on machine learning using a single camera.…”

Section: Introductionmentioning

confidence: 99%

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Efficient fall activity recognition by combining shape and motion features

2020

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This paper presents a vision-based system for recognizing when elderly adults fall. A fall is characterized by shape deformation and high motion. We represent shape variation using three features, the aspect ratio of the bounding box, the orientation of an ellipse representing the body, and the aspect ratio of the projection histogram. For motion variation, we extract several features from three blocks corresponding to the head, center of the body, and feet using optical flow. For each block, we compute the speed and the direction of motion. Each activity is represented by a feature vector constructed from variations in shape and motion features for a set of frames. A support vector machine is used to classify fall and non-fall activities. Experiments on three different datasets show the effectiveness of our proposed method.

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“…Human action recognition is a hot topic in computer vision and pattern recognition [1], which has been applied to various fields, such as medical health [2], intelligent space [3], interactive entertainment [4], robotics [5] and so on. The action information can be obtained from medical equipment to help patients with rehabilitation training.…”

Section: Introductionmentioning

confidence: 99%

S3GRN: Structural Similar Stepwise Generative Recognizable Network for Human Action Recognition With Limited Training Data

Shen

Liu

2020

IEEE Access

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Human action recognition is a hot topic and it has been applied to various fields. Deep learning is one of the techniques in human action recognition which has achieved good results. However, the task is still challenging due to the less collected samples. In order to address this challenge and improve the recognition accuracy, the stepwise generative recognizable network is proposed based on the generative adversarial network, which can be used to expand limited training samples and then recognize. Firstly, the stepwise generative recognizable network is designed to combine the function of images generation and recognition for human action. Secondly, the structural similar constraint is introduced to stepwise generative recognizable network, called structural similar stepwise generative recognizable network, which can compare the similarity of generated images with real data to improve quality and diversity of generated images. Finally, the performance of proposed networks is verified by common databases and the self-build database which is collected in daily life. We achieved 97.14%, 94.88% and 99.69% recognition accuracy on MNIST, Weizmann and self-build dataset, respectively. The experimental results show that the combination of generation and recognition can improve the recognition accuracy without abundant training data, and the structural similar constraint not only can improve the quality and diversity of generated images but also perform better in convergence. The structural similar stepwise generative recognizable network reduces the workload of manual collection and solves the problem of lower recognition accuracy for limited training samples, which achieves the characteristics of natural expanded samples.

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Human fall detection using slow feature analysis

Cited by 32 publications

References 37 publications

Fall Detection in Videos With Trajectory-Weighted Deep-Convolutional Rank-Pooling Descriptor

Fall Detection in Videos With Trajectory-Weighted Deep-Convolutional Rank-Pooling Descriptor

Efficient fall activity recognition by combining shape and motion features

S3GRN: Structural Similar Stepwise Generative Recognizable Network for Human Action Recognition With Limited Training Data

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