Multiple Feature Fusion in Particle Filter Framework for Visual Tracking

Abstract

Vision-based human activity recognition in smart homes has become a significant issue in terms of developing the next generation technologies Recently, deep learning models that aim to automatic extraction of low-level to high-level features of input data instead of using complicated conventional feature extraction methods have achieved significant improvements in the classification of a large amount of data especially vision-based datasets. Therefore, in this study, in order to recognize human action of a smart home video dataset. Convolutional neural networks (CNNs) architecture as a deep learning model has been proposed, and an architecture of CNNs has been proposed. Moreover, instead of using commonplace CNNs, a special CNN architecture to recognize human activity has been designed. Additionally, the performance of the proposed method has been compared with the other previous used methods on the same dataset.