Place Recognition Using Multiple Wearable Cameras
Recognizing a user’s location is the most challenging problem for providing intelligent location-based services. In this paper, we presented a real-time camera-based system for the place recognition problem. This system takes streams of scene images of a learned environment from user-worn cameras and produces the class label of the current place as an output. Multiple cameras are used to collect multi-directional scene images because utilizing multiple images yields better and robust recognition than a single image. For more robust recognition, we utilized spatial relationships between the places. In addition that, a temporal reasoning is incorporated with a Markov model to reflect typical staying time at each place. Recognition experiments, which were conducted in a real environment in a university campus, showed that the proposed method yields a very promising result.
Keywordscontext recognition place recognition image understanding wearable computing hidden Markov models
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