Indoor scene recognition by a mobile robot through adaptive object detection
Robotics and Autonomous Systems
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Places in an environment can be described by the objects they contain. This paper discusses the completely automated integration of object detection and place classification in a single system. We first perform automated learning of object-place relations from an online annotated database. We then train object detectors on some of the most frequently occurring objects. Finally, we use detection scores as well as learned object-place relations to perform place classification of images. We also discuss areas for improvement and the application of this work to informed visual search. As a whole, the system demonstrates the automated acquisition of training data containing labeled instances (i.e. bounding boxes) and the performance of a state-of-the-art object detection technique trained on this data to perform place classification of realistic indoor scenes.