A sample-free Bayesian-like model for indoor environment recognition.
Oyebode, Kazeem Du, Shengzhi Van Wyk, Barend Jacobus Djouani, Karim
Oyebode, Kazeem
Du, Shengzhi
Van Wyk, Barend Jacobus
Djouani, Karim
Citations
Altmetric:
Abstract
Visual localization of indoor environments enables an autonomous system to recognize its current location and environment using sensors such as a camera. This paper proposes a method for visual recognition of indoor environments leveraging on existing object detection, ontology, Bayesian-like framework, and speeded-up robust features (SURF) algorithms. Objects detected in such an environment are fed into a Bayesian-like framework for domain recognition. Finally, the SURF localizes the predicted environment. One of the objectives of the proposed model is to eliminate the image-based training phase encountered in traditional place recognition algorithms. The proposed model does not rely on any visual information on the environment for training. Experiments are carried out on two publicly available datasets with promising results.
Description
Date
2019-07-01
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Research Projects
Organizational Units
Journal Issue
Keywords
Bayesian reasoning, Image recognition, Image localization, Convolutional neural network