VOLUME 13 NUMBER 2 (July to December 2020)

PSL%202019 vol12-no02-p133-138-Mikita%20and%20Padlan

Philipp. Sci. Lett. 2020 13 (2) 183-189
available online: November 30, 2020

*Corresponding author
Email Address: anton_louise_deocampo@dlsu.edu.ph
Date received: June 17, 2020
Date revised: October 1, 2020
Date accepted: October 17, 2020


Vision-based human activity recognition via dispersion measures of spatiotemporal features

Anton Louise P. De Ocampo*1 and Elmer P. Dadios2

1Electronics and Communications Engineering Department,
      De La Salle University, Manila, Philippines
2Manufacturing Engineering and Management Department
      De La Salle University, Manila, Philippines
Human activity recognition (HAR) systems can be categorized as a sensor- or vision-based depending on the type of data it collects as inputs. The non-contact implementation of vision-based HAR is the confounding factor why such systems are preferred over systems using wearable sensors. Although remarkable feats have been achieved in the field of human activity recognition, one challenge remains the focus of recent researches –extraction and selection of suitable features. In this work, a novel approach in extracting and selecting feature vector for HAR implementation in smart farming is proposed. By exploiting the data distribution on stacks of difference maps, a new feature vector, which contains high discriminative attributes between activities performed by farmers in the field, is proposed. Using the k-NN classifier, the experiment obtained 98.89%, 98.69%, and 98.79% scores in precision, recall, and F1-measure in the classification of farmers’ activities in the field.

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