Authors:
Mubarak G. Abdu-Aguye
1
and
Walid Gomaa
2
Affiliations:
1
Computer Science and Engineering Department, Egypt-Japan University of Science and Technology, Alexandria and Egypt
;
2
Computer Science and Engineering Department, Egypt-Japan University of Science and Technology, Alexandria, Egypt, Faculty of Engineering, Alexandria University and Egypt
Keyword(s):
Activity Recognition, Deep Metric Learning, Convolutional Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Engineering Applications
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Problems in Signal Processing
;
Robotics and Automation
;
Sensors Fusion
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time-Frequency Analysis
Abstract:
In the domain of Activity Recognition, the proliferation of low-cost and sensor-enabled personal devices has led to significant heterogeneity in the data generated by users. Traditional approaches to this problem have previously relied on handcrafted features and template-matching methods, which have limited flexibility and performance with high variability. In this work we investigate the use of Deep Metric Learning in the domain of activity recognition. We use a deep Triplet Network to generate fixed-length descriptors from activity samples for purposes of classification. We carry out evaluation of our proposed method on five datasets from different sources with differing activities. We obtain classification accuracies of up to 96% in self-testing scenarios and up to 91% accuracy in cross-dataset testing without retraining. We also show that our method performs similarly to traditional Convolutional Neural Networks. The obtained results indicate the promise of this approach.