Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.12.1.2
High-Level Descriptors for Fall Event Detection Supported by a Multi-Stream Network
Sarah Almeida Carneiro
; Institute of Computing, University of Campinas, Campinas, SP, 13083-852, Brazil
Silvio Jamil Ferzoli Guimarães
; High-Level Descriptors for Fall Event Detection Supported by a Multi-Stream Network 11 Volume 12, Number 1, 2021 Original Scientific Paper Sarah Almeida Carneiro Institute of Computing, University of Campinas Campinas, SP, 13083-852, Brazil sarah.alcar@gmail.com Silvio Jamil Ferzoli Guimarães Computer Science Department, Pontifical Catholic University of Minas Gerais (PUC Minas) Belo Horizonte, MG, 30535-065, Brazil
Hélio Pedrini
; Institute of Computing, University of Campinas Campinas, SP, 13083-852, Brazil
Sažetak
The need for assertive video classification has been increasingly in demand. Especially for detecting endangering situations, it is crucial to have a quick response to avoid triggering more serious problems. During this work, we target video classification concerning falls. Our study focuses on the use of high-level descriptors able to correctly characterize the event. These descriptor results will serve as inputs to a multi-stream architecture of VGG-16 networks. Therefore, our proposal is based on the analysis of the best combination of high-level extracted features for the binary classification of videos. This approach was tested on three known datasets, and has proven to yield similar results as other more consuming methods found in the literature.
Ključne riječi
Video Classification; Multi-stream Network; Fall Detection; High Level Features; Convolutional Neural Network
Hrčak ID:
257517
URI
Datum izdavanja:
21.4.2021.
Posjeta: 523 *