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Type: Artigo de Periódico
Title: Real-time Action Recognition Using A Multilayer Descriptor With Variable Size
Author: Alcantara
MF; Moreira
TP; Pedrini
Abstract: Video analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveillance environments, such as forensic investigation, patient monitoring, medical training, accident prevention, and traffic monitoring, among others. We present a method for action identification based on adaptive training of a multilayer descriptor applied to a single classifier. Cumulative motion shapes (CMSs) are extracted according to the number of frames present in the video. Each CMS is employed as a self-sufficient layer in the training stage but belongs to the same descriptor. A robust classification is achieved through individual responses of classifiers for each layer, and the dominant result is used as a final outcome. Experiments are conducted on five public datasets (Weizmann, KTH, MuHAVi, IXMAS, and URADL) to demonstrate the effectiveness of the method in terms of accuracy in real time. (C) 2016 SPIE and IS&T
Subject: Adaptive Learning
Action Detection
Motion Silhouettes
Surveillance Systems
Real-time Video Analysis
Editor: IS&T & SPIE
Citation: Journal Of Electronic Imaging. IS&T & SPIE, n. 25, n. 1, p. .
Rights: fechado
Identifier DOI: 10.1117/1.JEI.25.1.013020
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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