Robust tracking of abrupt motion is a challenging task
in computer vision due to the large motion uncertainty. In
this paper, we propose a stochastic approximation Monte
Carlo (...
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors ...
This paper addresses the discovery of activities and learns the underlying processes that govern their occurrences over time in complex surveillance scenes. To this end, we propos...
Deformable model fitting has been actively pursued in the computer vision
community for over a decade. As a result, numerous approaches have
been proposed with varying degrees of...