Adaptive Human-In-The-Loop Multi-Target Recognition Improved By Learning

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.