Finding Most Influential Places Using AR, VPS, Game Theory and Deep Learning Techniques

Nowadays, navigation systems play an important role in real-life scenarios. Finding the most influential place in a locality can be a complex and cumbersome task. It involves various factors such as, the sort of outdoor location, age range (toddlers, teens, middle-aged, and elderly), approximate population size, and gender diversity. In this paper, we propose a model to obtain the top-k most influential places. The proposed model is classified into two folds: finding information about location based on image classification and finding top K place information based on game theory. In the first fold, we used the R-CNN + EfficientNet models for image classification. In the second fold, we have used the Shapley value (a game theoretic approach) for finding the top K place selection. Now we have introduced the visual positioning system (VPS) to find accurate navigation to particular places.