Title: Performance evaluation of entropy-based cuboid selection technique for human interaction recognition
Authors: Aditi Jahagirdar
Addresses: School of Computer Engineering and Technology, MIT World Peace University, Pune, Maharashtra, 411038, India
Abstract: Local feature-based approaches are proven to be most successful for the application of human action recognition. This work aims to study the effect of the number of cuboids chosen on the performance of human action recognition. A 3D region, called cuboid, is extracted around every spatio-temporal interest point and an entropy-based cuboid selection method is implemented for choosing the cuboids having maximum information. The proposed method is evaluated using UT interaction and SBU kinect interaction datasets. Results show that maximum classification accuracy and F1 Score are obtained when the top 40 cuboids are selected for the feature extraction. The classification accuracy and F1 Score increase when the number of selected cuboids is increased from 10 to 40 and remain constant when the number is increased beyond 40. The selected 40 cuboids are then used for feature extraction. To carry out this experiment, wavelet coefficients extracted from selected cuboids are used as features.
Keywords: cuboids; discrete wavelet transform; entropy; human action recognition; interaction detection; spatio temporal interest points.
International Journal of Intelligent Systems Technologies and Applications, 2022 Vol.20 No.6, pp.453 - 468
Received: 19 Jan 2022
Accepted: 05 May 2022
Published online: 25 Jan 2023 *