Silhouette Orientation Volumes for Efficient Fall Detection in Depth Videos

Autor: Abdulkadir Sengu, Muzaffer Aslan, Erdem Akagunduz, Haibo Wang, Melih Cevdet Ince
Rok vydání: 2017
Předmět:
Male
Databases
Factual
Feature extraction
Video Recording
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Monitoring
Ambulatory
Poison control
02 engineering and technology
Pattern Recognition
Automated
Silhouette
Naive Bayes classifier
Bayes' theorem
Health Information Management
Robustness (computer science)
Image Processing
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
Humans
Computer vision
Electrical and Electronic Engineering
Orientation (computer vision)
business.industry
Bayes Theorem
020207 software engineering
Pattern recognition
Computer Science Applications
ComputingMethodologies_PATTERNRECOGNITION
Accidental Falls
Female
020201 artificial intelligence & image processing
Artificial intelligence
Fall detection
business
Algorithms
Biotechnology
Zdroj: IEEE Journal of Biomedical and Health Informatics. 21:756-763
ISSN: 2168-2208
2168-2194
DOI: 10.1109/jbhi.2016.2570300
Popis: A novel method to detect human falls in depth videos is presented in this paper. A fast and robust shape sequence descriptor, namely the Silhouette Orientation Volume (SOV), is used to represent actions and classify falls. The SOV descriptor provides high classification accuracy even with a combination of simple associated models, such as Bag-of-Words and the Naïve Bayes classifier. Experiments on the public SDU-Fall dataset show that this new approach achieves up to 91.89% fall detection accuracy with a single-view depth camera. The classification rate is about 5% higher than the results reported in the literature. An overall accuracy of 89.63% was obtained for the six-class action recognition, which is about 25% higher than the state of the art. Moreover, a perfect silhouette-based action recognition rate of 100% is achieved on the Weizmann action dataset.
Databáze: OpenAIRE
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