Event Location: NSH 1305
Bio: Greg Mori was born in Vancouver, BC. He received the Hon. B.Sc. degree
with high distinction in computer science and mathematics from the
University of Toronto in 1999, and the Ph.D. degree in computer science
from the University of California at Berkeley in 2004. At Berkeley, he
was supported in part by a UC Berkeley Regents’ Fellowship. He is
currently an Assistant Professor in the School of Computing Science at
Simon Fraser University. His research interests are in computer vision,
and include human body pose estimation, activity recognition, shape
matching, and object recognition.

Abstract: In this talk we present two pieces of work in the “Looking at People”
domain. In the first part, we address the problem of detecting
pedestrians in still images. We introduce an algorithm for learning
shapelet features, a set of mid-level features. These features are
focused on local regions of the image and are built from low-level
gradient information that discriminates between pedestrian and
non-pedestrian classes. Using AdaBoost, these shapelet features are
created as a combination of oriented gradient responses. To train the
final classifier, we use AdaBoost for a second time to select a subset
of our learned shapelets. By first focusing locally on smaller
feature sets, our algorithm attempts to harvest more useful
information than by examining all the low-level features together.
We present quantitative results demonstrating the effectiveness of our
algorithm. In particular, we obtain an error rate 14 percentage
points lower (at $10^{-6}$ FPPW) than the previous state of the art
detector of Dalal and Triggs on the INRIA dataset.

In the second part, we present a method for estimating human pose in still
images. Tree-structured models have been widely used for this problem.
While such models allow efficient learning and inference, they fail to
capture additional dependencies between body parts, other than kinematic
constraints. In this paper, we consider the use of multiple tree models,
rather than a single tree model for human pose estimation. Our model can
alleviate the limitations of a single tree-structured model by combining
information provided across different tree models. The parameters of each
individual tree model are trained via standard learning algorithms in a
single tree-structured model. Different tree models are combined in a
discriminative fashion by a boosting procedure. We present experimental
results showing the improvement of our model over previous approaches on a
very challenging dataset.