Human fall detection based on block matching and silhouette area

Gnouma, Mariem; Ejbali, Ridha; Zaied, Mourad

doi:10.1117/12.2268988

Cited by 8 publications

(1 citation statement)

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“…The ratio and differences of width and height of the bounding box surrounding human are used by Liu et al [14] as shape features to classify postures using k-nearest neighbor classifier. The silhouette area of the elderly person and block matching for motion estimation are exploited by Gnouma et al [15] to classify fall events. Kamal et al [16] extracted the person silhouette based on background subtraction technique and then a set of features were measured such as the vertical velocity of the head, area, height/width ratio, orientation.…”

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%