Spring 2011 GRASP Seminar - Pedro Felzenszwalb, University of Chicago, "Object Detection with Discriminatively Trained Part Based Models"
Friday, February 18, 2011 - 11:00am to 12:00pm
Abstract: Object detection is one of the fundamental challenges in computer
vision. In this talk I will consider the problem of detecting objects
from a generic category, such as people or cars, in static images. This
is a difficult problem because objects in such categories can vary
greatly in appearance. For example, people wear different clothes and
take a variety of poses while cars come in various shapes and colors.
We have built an object detection system that addresses this challenge
using mixtures of deformable part models. The system is both highly
efficient and accurate, achieving state-of-the-art results on the
PASCAL object detection benchmarks. We train our models from weakly
labeled data using a discriminative procedure that we call latent SVM.
A latent SVM leads to a non-convex training problem. However, a latent
SVM is semi-convex and the training problem becomes convex once latent
information is specified for the positive examples. This leads to an
iterative algorithm for solving the training problem. I will also
discuss a general framework for object detection using rich visual
Joint work with Ross Girshick, David McAllester and Deva Ramanan
Pedro F. Felzenszwalb is an Associate Professor at the University of
Chicago. He received his PhD from MIT in 2003. His main research
interests are in computer vision, geometric algorithms and artificial
intelligence. His work has been supported by the National Science
Foundation, including a CAREER award received in 2008. He is currently
serving as a program chair for the 2011 IEEE CVPR. He is an Associate
Editor of the IEEE Transactions on Pattern Analysis and Machine
Intelligence and an Editorial Board Member for the International
Journal of Computer Vision. In 2010 he received the IEEE CVPR
Longuet-Higgins Prize for fundamental contributions to computer vision
and the PASCAL Visual Object Challenge "Lifetime Achievement" prize.