A Computer Vision Based Fall Detection Technique for Home Surveillance

Sree, Katamneni Vinaya; Jeyakumar, G.

doi:10.1007/978-3-030-37218-7_41

Cited by 7 publications

(1 citation statement)

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“…Optical flow sequences were used as input to the networks followed by a three training phases. Fall detection system for home surveillance was proposed by [ 49 ]. A surveillance video was used to detect the fall.…”

Section: Applications Of Action Recognition Modelsmentioning

confidence: 99%

A Review on Computer Vision-Based Methods for Human Action Recognition

et al. 2020

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Human action recognition targets recognising different actions from a sequence of observations and different environmental conditions. A wide different applications is applicable to vision based action recognition research. This can include video surveillance, tracking, health care, and human–computer interaction. However, accurate and effective vision based recognition systems continue to be a big challenging area of research in the field of computer vision. This review introduces the most recent human action recognition systems and provides the advances of state-of-the-art methods. To this end, the direction of this research is sorted out from hand-crafted representation based methods including holistic and local representation methods with various sources of data, to a deep learning technology including discriminative and generative models and multi-modality based methods. Next, the most common datasets of human action recognition are presented. This review introduces several analyses, comparisons and recommendations that help to find out the direction of future research.

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Section: Applications Of Action Recognition Modelsmentioning

confidence: 99%