Abstract
The rapid development of Information and Communication Technology (ICT) enhances the government services to its citizen. As a consequence affects the exchange of information between citizen and government. However, some groups can experience some difficulties accessing the government services due to disabilities, such as blind people as well as material and geographical constraints. This paper aims to introduce a novel approach for place recognition to help the blind people to navigate inside a government building based on the correlation degree for the covariance feature vectors. However, this approach faces several challenges. One of the fundamental challenges inaccurate indoor place recognition for the visual impaired people is the presence of similar scene images in different places in the environmental space of the mobile robot system, such as a computer or office table in many rooms. This problem causes bewilderment and confusion among different places. To overcome this, the local features of these image scenes should be represented in more discriminatory and robustly way. However, to perform this, the spatial relation of the local features should be considered. The findings revealed that this approach has a stable manner due to its reliability in the place recognition for the robot localization. Finally, the proposed Covariance approach gives an intelligent way for visual place people localization through the correlation of Covariance feature vectors for the scene images.
Highlights
The development of new technologies might prove to be a great facilitator for the integration of disabled people provided that these environments are accessible, usable and useful; in other words, that they take into consideration the various characteristics of the activity and the needs and particularities related to the disability of the users (Paciello, 2000)
To demonstrate the accuracy performance of Covariance Minimum Distance (CMD), the algorithm implemented on various illumination condition groups for IDOL dataset each group divided into two parts such as train and test images, each parts divided into 16 subgroups
Place recognition based on CMD gives some reliability and accurate perception for the global localization and it reduces the confusion for place recognition
Summary
The development of new technologies might prove to be a great facilitator for the integration of disabled people provided that these environments are accessible, usable and useful; in other words, that they take into consideration the various characteristics of the activity and the needs and particularities (cognitive, perceptive, or motive) related to the disability of the users (Paciello, 2000). One of the fundamental problems in the visual place recognition is the confusion of matching visual scene image with the stored database images. This problem is caused by instability of local feature representation. Machine learning is used to improve the localization process of known or unknown environments. The process of quantizing the features is quite similar to the BOW model as in (Uijlings et al, 2009) These visual words do not possess spatial relations. This model employed to make more accurate features for describing the scene image in place recognition
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