February 28, 2017

1:30 pm / 2:30 pm

Venue

Clark Hall 314

Dynamics-based Invariants for Multi-Camera Video Understandingn
The power of geometric invariants to provide solutions to computer vision problems has been recognized for a long time. On the other hand, dynamics-based invariants are often overlooked. Yet, visual data come in streams: videos are temporal sequences of frames, images are ordered sequences ofrows of pixels and contours are chained sequences of edges. In this talk,I will discuss the key role that systems theory can play in timely extracting and exploiting dynamics-based invariants to capture actionable information that is very sparsely encoded in high dimensional data streams. The central theme of this approach is the use of dynamical models, and their associated invariants, as an information-encoding paradigm. We will show that embedding problems in the conceptual world of dynamical systems makes available a rich, extremely powerful resource base, leading to robust solutions, or, in cases where the underlying problem is intrinsically hard, to computationally tractable approximations with sub optimality certificates. We will illustrate these ideas in the context of three practical applications: crowd-sourcing video, activity recognition, and human re-identification

http://www.coe.neu.edu/people/camps-octavia