Faster Bounding Box Annotation for Object Detection in Indoor Scenes
| Autor: | Heikki Huttunen, Bishwo N. Adhikari, Jussi Puura, Jukka Peltomaki |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) 02 engineering and technology 010501 environmental sciences 01 natural sciences Two stages Machine Learning (cs.LG) Annotation Minimum bounding box Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering 0105 earth and related environmental sciences Class (computer programming) business.industry Deep learning Pattern recognition Workload Object (computer science) Object detection 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | EUVIP |
| Popis: | 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. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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