Human Activity Recognition Using Gaussian Mixtures
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
Objective of recognizing human activity in the growing ubiquity and mobility of video data has emerged as one of the key research topic in computer vision and machine learning. This paper considers the inclusivity of latent variables by extracting missing information with discriminative features in reduced feature space. Multi-modal distribution analysis is performed using Gaussian mixture model in maximum likelihood basis to obtain Gaussian mixtures, PCA is applied to these mixtures to preserve suitable discriminatory features in compressed space. Different distance measure techniques are used to classify and recognize human activity. Proposed subspace mixture model achieved most promising results on KTH dataset in comparison with few state-of-the-art techniques.
Authors and Affiliations
Mahantesh K, Namita H, Harshitha R, Madhurya S, Nischitha K S
Keywords
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