Sarah gives a talk at ECE, UMass Dartmouth
Friday October 26
Title: Human Pose Estimation: Deep Learning with Small Data
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.