Adaptive visual models for finding people (& other objects)
Speaker: Deva Ramanan , Professor, Toyota Technological Institute at ChicagoContact:
Date: October 26 2007
Time: 4:00PM to 5:00PM
Location: Star Seminar Room (32-D463)
Host: C. Mario Christoudias, Gerald Dalley, MIT CSAIL
C. Mario Christoudias, Gerald Dalley, 3-4278, 3-6095, email@example.com, firstname.lastname@example.orgRelevant URL:
In this talk I will describe a family of algorithms for object detection, with a focus on finding people in images and video. Object detection is hard because objects can vary in appearance due to 3D pose and illumination (among other factors). The visual appearance of people is further complicated by articulated pose and clothing. One approach is to build models that are invariant to such changes; another is to build models that adapt. Part-based models that geometrically deform `on-the-fly' are an example of an adaptive approach. I will describe approaches that also adapt photometrically.
In the context of finding people in video, such approaches simultaneously learn the color of a person's clothes while tracking him/her. In the context of object detection in images, such approaches simultaneously segment and detect an object. Some issues need to be addressed - what is the computational algorithm for efficiently adapting `on-the-fly'? How does one train such an algorithm? I will discuss a few schemes.
Extensive experimental results will the shown throughout the talk, including large-scale detection and tracking on terabytes of video data and several benchmark object detection datasets, yielding state-of-the-art results.
Deva Ramanan is currently both an assistant professor in Computer Science at the University of California at Irvine and a research professor at the Toyota Technological Institute at Chicago. He received the BS degree with distinction, summa cum laude, from the University of Delaware. He received the PhD degree in Electrical Engineering and Computer Science with a Designated Emphasis in Communications, Computation, and Statistics from the University of California at Berkeley. He has been an academic visitor with the Visual Geometry Group at Oxford University and a consultant for the Robotics Institute at Carnegie Mellon University. His research has been supported by a University of California MICRO Fellowship and by a US National Science Foundation Graduate Research Fellowship. His interests span computer vision, machine learning, and computer graphics. His graduate work focused on tracking people and recognizing their activities.
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