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- W3214966428 abstract "AbstractThe task of Human Fall detection is taken up to develop efficient technology to assist humans in activities of their daily living. In homes, the aged people are more prone to fall with improper gait, due to weakened muscles, lack of balance, sensation and co-ordination. The toddlers frequently fall down as the above abilities are not yet well developed in them. The elderly people and the toddlers need constant watch in homes by a system to alert the care-takers immediately after a fall, that makes our homes smart and safe. This research addresses this problem. In this work, we use motion information obtained from video to compute new temporal templates, which are in turn used by a convolutional neural network to recognize the human actions. A new representation capturing the pose of the subject over a period of time is proposed for human action recognition. This temporal template representation computed at the beginning and at the end of the fall-event is explored in this work for fall detection. The ConvNet feature extracted from these temporal templates are used by an extreme learning machine (ELM) for action recognition. The efficacy of the proposed approach is demonstrated on SDU Fall detection, UP-Fall, UR Fall, and MIVIA action datasets.KeywordsFall detectionDepth videoTemporal templateConvolutional neural networkExtreme learning machineSDU Fall datasetUP-Fall datasetUR Fall detection datasetMIVIA Action dataset" @default.
- W3214966428 created "2021-12-06" @default.
- W3214966428 creator A5061023457 @default.
- W3214966428 date "2021-12-01" @default.
- W3214966428 modified "2023-09-26" @default.
- W3214966428 title "Human Fall Detection in Depth-Videos Using Temporal Templates and Convolutional Neural Networks" @default.
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- W3214966428 doi "https://doi.org/10.1007/978-3-030-87132-1_10" @default.
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