Fall detection based on fused saliency maps

Liu, Hongjun; Li, Chaobo; Ding, Yupeng

doi:10.1007/s11042-020-09708-6

Cited by 18 publications

(7 citation statements)

References 44 publications

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“…The maximum work for fall detection using DL have been done using CNN followed by hybrid, LSTM, Auto-encoder and MLP as shown in Figure 20. These DL based Classification of papers based on the CNN modle used CNN [39,41,44,20,52,45,54,59,61,63,64,65,66,67,68,8,69,71,73,74,76,77,78,79,80,81,82,84,87,88,89,90,91,36,92,93,94,95,99,100,101,102,103,104,107,105,108,106] LSTM [117,…”

Section: Discussion On Limitations and Future Scopementioning

confidence: 99%

See 1 more Smart Citation

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Alam¹,

Sufian²,

Dutta³

et al. 2022

Preprint

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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-thearts 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.

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Section: Discussion On Limitations and Future Scopementioning

confidence: 99%

“…The output of the FC layer was fed to softmax for final fall detection. Li et al (2021) [108] introduced fused saliency-based fall detection system. This system had two parts namely the saliency maps generation part and fall detection part.…”

Section: Cnn Based Techniquesmentioning

confidence: 99%

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Alam¹,

Sufian²,

Dutta³

et al. 2022

Preprint

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“…In the work presented in [26], the proposed solution com- prised two components: a saliency maps generation model and a fall detection model. The saliency maps generation model was employed to mitigate the complexity of the frames.…”

Section: Related Workmentioning

confidence: 99%

Activity Classification and Fall Detection Using Monocular Depth and Motion Analysis

Mobsite,

Alaoui,

Boulmalf

et al. 2024

IEEE Access

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In the realm of senior care, falls remain the primary cause of hospitalization and mortality. Early detection can facilitate timely medical interventions and mitigate the associated risks. Hence, it is beneficial to set up a mechanism to identify and track such incidents. While wearable sensors are commonly used for this purpose, they may not be feasible for daily use. In contrast, surveillance cameras provide an unobtrusive and convenient monitoring solution. Despite the intricate nature of video frames, employing deep learning techniques for human recognition can enhance the performance of such systems. In our approach, activity classification is triggered only when a person is detected in the environment. We then process the extracted Global History of Motion (GHM) images with our Lightweight Deep Neural Network (LDNN). Additionally, we employ a dilated Convolution Long Short Term Memory (ConvLSTM) coupled with our LDNN to analyze the extracted sequence of monocular depth frames. Ultimately, the decision is made based on the combined information from both streams. Empirical results demonstrate the good performance of our fall detection system on the UP-Fall and UR datasets compared to the state-of-the-art. In terms of person classification, the Xception network achieved an F1-score of 98.46% on a sub-dataset derived from the COCO dataset, and 100% on the UP-Fall dataset.

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“…In the context based visual saliency detection algorithm, multiscale correction and context perception are combined so that the saliency of the target area in the saliency map is the highest, but the more it spreads to the edge, the lower the saliency, and at the edge, the image saliency is the most blurred [23,24].…”

Section: Context-based Visual Saliency Detection Algorithmmentioning

confidence: 99%

“…It calculates saliency according to global contrast and has a fast calculation speed. It has a good effect on image resources with less complex background information [24]. e two inputs ensure the validity and completeness of the module's information.…”

Section: Visual Saliency Detection Algorithm Based On Histogram Contrastmentioning

confidence: 99%

An Image Saliency Detection Method Based on Combining Global and Local Information

Yang

Gong

Wang

2022

Mathematical Problems in Engineering

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In the field of computer vision, image saliency target detection can not only improve the accuracy of image detection but also accelerate the speed of image detection. In order to solve the existing problems of the saliency target detection algorithms at present, such as inconspicuous texture details and incomplete edge contour display, this paper proposes a saliency target detection algorithm integrating multiple information. The algorithm consists of three processes: preprocessing process, multi-information extraction process, and fusion optimization process. The frequency domain features of the image are calculated, the algorithm calculates the frequency domain features of the image, introduces power law transform and feature normalization, improves the frequency domain features of the image, saves the information of the target region, and inhibits the information of the background region. On three public MSRA, SED2, and ECSSD image datasets, the proposed algorithm is compared with other classical algorithms in subjective and objective comparison experiments. Experimental results show that the proposed algorithm can not only accurately and comprehensively extract significant target regions but also retain more texture information and complete edge information while satisfying the human visual experience. All evaluation indexes are significantly better than the comparison algorithm, showing good reliability and adaptability.

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Fall detection based on fused saliency maps

Cited by 18 publications

References 44 publications

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Activity Classification and Fall Detection Using Monocular Depth and Motion Analysis

An Image Saliency Detection Method Based on Combining Global and Local Information

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