Fusing Handcrafted and Contextual Features for Human Activity Recognition
Ημερομηνία
2019Γλώσσα
en
Λέξη-κλειδί
Επιτομή
In this paper we present an approach for the recognition of human activity that combines handcrafted features from 3D skeletal data and contextual features learnt by a trained deep Convolutional Neural Network (CNN). Our approach is based on the idea that contextual features, i.e., features learnt in a similar problem are able to provide a diverse representation, which, when combined with the handcrafted features is able to boost performance. To validate our idea, we train a CNN using a dataset for action recognition and use the output of the last fully-connected layer as a contextual feature representation. Then, a Support Vector Machine is trained upon an early fusion step of both representations. Experimental results prove that the proposed method significantly improves the recognition accuracy in an arm gesture recognition problem, compared to the use of handcrafted features only. © 2019 IEEE.
Collections
Related items
Showing items related by title, author, creator and subject.
-
Resource-efficient TDNN Architectures for Audio-visual Speech Recognition
Koumparoulis A., Potamianos G., Thomas S., da Silva Morais E. (2021)In this paper, we consider the problem of resource-efficient architectures for audio-visual automatic speech recognition (AVSR). Specifically, we complement our earlier work that introduced efficient convolutional neural ... -
Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters
Poularakis S., Katsavounidis I. (2016)In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for ... -
An Audiovisual Child Emotion Recognition System for Child-Robot Interaction Applications
Filntisis P.P., Efthymiou N., Potamianos G., Maragos P. (2021)We present an audiovisual emotion recognition system tailored to child-robot interaction scenarios. Our proposed system is based on deep learning and the Temporal Segment Networks framework, receives input from both the ...