Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors

Applied Sciences

Katarzyna Gościewska¹  Dariusz Frejlichowski¹ 

[1] Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Zolnierska 52, 71-210 Szczecin, Poland;

关键词: shape analysis;    foreground occlusion;    action recognition;    exercise classification;    silhouette sequences;   

DOI  :  10.3390/app11188633

来源: DOAJ

【 摘 要 】

This paper presents an action recognition approach based on shape and action descriptors that is aimed at the classification of physical exercises under partial occlusion. Regular physical activity in adults can be seen as a form of non-communicable diseases prevention, and may be aided by digital solutions that encourages individuals to increase their activity level. The application scenario includes workouts in front of the camera, where either the lower or upper part of the camera’s field of view is occluded. The proposed approach uses various features extracted from sequences of binary silhouettes, namely centroid trajectory, shape descriptors based on the Minimum Bounding Rectangle, action representation based on the Fourier transform and leave-one-out cross-validation for classification. Several experiments combining various parameters and shape features are performed. Despite the presence of occlusion, it was possible to obtain about 90% accuracy for several action classes, with the use of elongation values observed over time and centroid trajectory.

【 授权许可】

Unknown