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arXiv · 1807.03142

Faster Bounding Box Annotation for Object Detection in Indoor Scenes

Bishwo Adhikari·Jukka Peltomäki·Jussi Puura·Heikki Huttunen
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Abstract

This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations. We experimentally study which first/second stage split minimizes to total workload. In addition, we introduce a new fully labeled object detection dataset collected from indoor scenes. Compared to other indoor datasets, our collection has more class categories, different backgrounds, lighting conditions, occlusion and high intra-class differences. We train deep learning based object detectors with a number of state-of-the-art models and compare them in terms of speed and accuracy. The fully annotated dataset is released freely available for the research community.

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Bishwo Adhikari, Jukka Peltomäki, Jussi Puura, Heikki Huttunen. 2018-07-03. Faster Bounding Box Annotation for Object Detection in Indoor Scenes. https://doi.org/10.1109/euvip.2018.8611732

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