UPLIFT: Unsupervised Person Labeling and Identification via Cooperative Learning with Mobile Robots

As robots are widely used in assisting manual tasks, an interesting challenge is: Can mobile robots help create a labeled knowledge dataset that can be used for efficiently creating deep learning models for other sensors? This paper proposes an Unsupervised Person Labeling and Identification (UPLIFT) framework to automatically enlarge the labeled knowledge dataset. Typically, manual data labeling is very costly, especially when the user population is large and dynamic. To reduce the cost, we use a mobile robot to serve as a knowledge seed and to provide the pseudo-ground-truth for the system so that unlabeled images from other fixed surveillance cameras can be paired with the pseudo-ground-truth. Ultimately, the knowledge dataset can be generated via a system-to-system knowledge transfer process from the former to the latter and gradually expanded as the system operates longer. Experimental results in two environments indicate that UPLIFT achieves an accuracy of 94.1% on average to detect pedestrians' IDs every 10 seconds.