University Events

Description: Topic: HUMAN POSE ESTIMATION: DEEP LEARNING WITH SMALL DATA

Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A

Abstract:
Although human pose estimation for various computer vision (CV) applications has been studied extensively in the last few decades, yet some pose problem such as in-bed pose estimation has been ignored by the CV community because it is assumed to be identical to the general purpose pose estimation methods. However, in-bed pose estimation has its own specialized aspects and comes with specific challenges including the notable differences in lighting conditions throughout a day and also having different pose distribution from the common human surveillance viewpoint. In this talk, I show that these challenges significantly lessen the effectiveness of the existing general purpose pose estimation models. In order to address the lighting variation challenge, infrared selective (IRS) image acquisition technique has been used by my lab to provide uniform quality data under various lighting conditions. In addition, to deal with unconventional pose perspective, a 2-end histogram of oriented gradient rectification method is presented. Deep learning framework proves to be the most effective model in human pose estimation, however the lack of large public dataset for in-bed poses prevents us from using a large network from scratch. In this work, we explored the idea of employing a pre-trained convolutional neural network (CNN) model trained on large public datasets of general human poses and fine-tuning the model using our own small (limited in size and different in perspective and color) in-bed IRS dataset.

Biography:
Sarah Ostadabbas is an assistant professor in the Electrical and Computer Engineering Department of Northeastern University (NEU), Boston, Massachusetts, USA. Sarah joined NEU in 2016 from Georgia Tech, where she was a post-doctoral researcher following completion of her PhD at the University of Texas at Dallas in 2014. At NEU, Sarah is the director of the Augmented Cognition Laboratory (ACLab) with the goal of enhancing human information-processing capabilities through the design of adaptive interfaces via physical, physiological, and cognitive state estimation. These interfaces are based on rigorous models adaptively parameterized using machine learning and computer vision algorithms. In particular, she has been integrating domain knowledge with machine learning by using physics-based simulation as generative models for bootstrapping deep learning recognizers. Professor Ostadabbas is the co-author of more than 50 peer-reviewed journal and conference articles and is an inventor on two US patent applications. She is the co-organizer of the Multimodal Data Fusion (MMDF2018) workshop, an NSF mini-workshop on deep learning in small data, an associate editor of the IEEE Transactions on Biomedical Circuits and Systems, on the Editorial Board of the Digital Biomarkers Journal, and has been serving in several signal processing and machine learning conferences as a technical chair or session chair. Professor Ostadabbas is a member of IEEE, IEEE Women in Engineering, IEEE Signal Processing Society, IEEE EMBS, IEEE Young Professionals, and ACM SIGCHI.

The Seminars is open to the public free of charge.

*For further information, please contact Dr. Honggang Wang at 508.999.8469, or by via email at hwang1@umassd.edu.