期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2018
卷号：15
期号：3
DOI：10.1177/1729881418774222
语种：English
出版社：SAGE Publications
摘要：Machine learning algorithms have been designed to address the challenge of multi-target recognition in dynamic and complex environments. However, sufficient high-quality samples are not always available for training an accurate multi-target recognition classifier. In this article, we propose a generic human-in-the-loop multi-target recognition framework that has four collaborative autonomy levels, and it allows adaptive autonomy level adjustment based on the recognition task complexity as well as the human operator’s performance. The human operator can intervene to relabel the collected data and guarantee the recognition accuracy when the trained classifier is not good enough. Meanwhile, the relabeled data are used for online learning which further improves the performance of the classifier. Experiments have been carried out to validate the proposed approach.
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