VASC Seminar : Visual Analysis of Crowded Scenes

Speaker :
Saad Ali
University of Central Florida
Thursday, April 3, 3:30pm, NSH 1307

Abstract:
Automatic localization, tracking, and event detection in videos ofcrowded environments is an important visual surveillance problem.Despite the sophistication of current surveillance systems, they havenot yet attained the desirable level of applicability and robustnessrequired for handling crowded scenes like parades, concerts, footballmatches, train stations, airports, city centers, malls etc.
In this talk, I will first present a framework for segmenting scenesinto dynamically distinct crowd regions using Lagrangian particledynamics. For this purpose, the spatial extent of the video is treatedas a phase space of a non-autonomous dynamical system where transportfrom one region of the phase space to the other is controlled by theoptical flow. A grid of particles is advected through the phase spaceusing the optical flow using a numerical integration scheme, and theamount by which neighboring particles diverge is quantified by using aCauchy-Green deformation tensor. The maximum eigenvalue of this tensoris used to construct a Finite Time Lyapunov Exponent (FTLE) field,which reveals the time-dependent invariant manifolds of thenon-autonomous dynamical system which are called Lagrangian CoherentStructures (LCS). The LCS in turn divides the crowd flow into regionsof different dynamics, and therefore are used to the segment the sceneinto distinct crowd regions. This segmentation is then used to detectany change in the behavior of the crowd over time. Next, I willpresent an algorithm for tracking individual targets in high density(hundreds of people) crowded scenes. The novelty of the algorithm liesin a scene structure based force model, which is used in conjunctionwith the available appearance information for tracking individuals in a complex crowded scene. The key ingredients of the scene structureforce model are three fields namely, `Static Floor Field' (SFF),`Dynamic Floor Field' (DFF), and `Boundary Floor Field' (BFF). Thesefields determine the probability of a person moving from one locationto another in a way that the object movement is more likely in thedirection of higher fields.

Bio:Saad Ali is currently a PhD candidate at the University of CentralFlorida, advised by Prof. Mubarak Shah. His research interests includesurveillance in crowded and aerial scenes, action recognition, objectrecognition and dynamical systems. He is a student member of IEEE.