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Quantized Feature with Angular Displacement for Activity Recognition
Author(s) -
YAMABE TOMOAKI,
KATAOKA HIROKATSU,
NAKAMURA AKIO
Publication year - 2016
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.11846
Subject(s) - angular displacement , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , quantization (signal processing) , displacement (psychology) , action recognition , computer vision , computer science , feature extraction , mathematics , geometry , psychology , philosophy , linguistics , psychotherapist , class (philosophy)
SUMMARY We propose quantized feature with angular displacement for pose‐based activity recognition. We calculate a three‐dimensional (3D) joint angle from three postural coordinates. The angular displacement should be quantized since joint angle includes errors due to system noises and similar posture. To investigate appropriate features, we propose four kinds of quantization levels; binarization, ternarization, quaternarization, and quinarization. We apply quantized features in order to improve pose‐based activity recognition with the UTKinect‐Action Dataset. In the experiment, we show the appropriate feature for activity recognition. As the result, the ternarized feature achieves the highest recognition rate in average. The recognition rate of trials with ternarized feature is improved 2.4% to one with no‐quantized feature, and 1.8% to conventional method.