Context awareness system in the use phase of a smart mobility platform: A vision system for a light-weight approach

Abstract Due to the development of image processing over the last years, the application of vision systems and image recognition technologies has been increasingly applied in many fields. In this paper, Machine Learning and Principal Component Analysis are utilized for context awareness at a smart mobility platform. The restrictions concern a rather dynamic environment and a relevantly small dataset of images. The aim is to determine the position of the camera itself and the appearance of obstacles within a predefined area. To examine the performance of the proposed approach Hierarchical Cluster Analysis has been applied to group similar observations into clusters.

[1]  Bram Klievink,et al.  Designing context-aware systems: A method for understanding and analysing context in practice , 2019, J. Log. Algebraic Methods Program..

[2]  Hao Peng,et al.  Improving Orbit Prediction Accuracy through Supervised Machine Learning , 2018, ArXiv.

[3]  Lyudmila Sukhostat,et al.  Lithological facies classification using deep convolutional neural network , 2019, Journal of Petroleum Science and Engineering.

[4]  Marco Loog,et al.  A benchmark and comparison of active learning for logistic regression , 2016, Pattern Recognit..

[5]  Yu Zhao,et al.  Identification and classification of explosives using semi-supervised learning and laser-induced breakdown spectroscopy. , 2019, Journal of hazardous materials.

[6]  Fabio Roli,et al.  Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2017, Pattern Recognit..

[7]  Julio Ortega Lopera,et al.  PCA filtering and probabilistic SOM for network intrusion detection , 2015, Neurocomputing.

[8]  George Michalos,et al.  A Machine Learning Approach for Visual Recognition of Complex Parts in Robotic Manipulation , 2017 .

[9]  George Chryssolouris,et al.  A neural network approach for the development of modular product architectures , 2011, Int. J. Comput. Integr. Manuf..

[10]  Nuno Horta,et al.  Reinforcement learning applied to Forex trading , 2018, Appl. Soft Comput..

[11]  Paulo Roberto Gardel Kurka,et al.  Applications of image processing in robotics and instrumentation , 2019, Mechanical Systems and Signal Processing.

[12]  Chih-Fong Tsai,et al.  Soft estimation by hierarchical classification and regression , 2017, Neurocomputing.