Event Location: NSH 1507

Abstract: The amount of digital video has grown exponentially in recent years. We are
at a nexus in time where video capture technology, computing power, storage
capacity, and broadband networking have matured sufficiently to fuel an
explosion in consumer videos. A key part of this ecosystem is the ability to
search over vast amounts of video data. While traditional methods have
relied on text, such as those extracted from closed captioning, speech
analysis, or manual annotation, we would like search based on the automated
recognition of the visual events in the video. This would enable more
general searches to be performed without relying on previously labeled data.
We propose a method for visual event detection of human actions that occur
in crowded, dynamic environments. Crowded scenes pose a difficult challenge
for current approaches to video event detection because it is difficult to
segment the actor from the background due to distracting motion from other
objects in the scene. We propose a technique for event recognition in
crowded videos that reliably identifies actions in the presence of partial
occlusion and background clutter. Our approach is based on three key ideas:
(1) we efficiently match the volumetric representation of an event against
over-segmented spatio-temporal video volumes; (2) we augment our shape-based
features using flow; (3) rather than treating an event template as an atomic
entity, we separately match by parts (both in space and time), enabling
robustness against occlusions and actor variability. Our experiments on
human actions, such as picking up a dropped object or waving in a crowd show
reliable detection with few false positives.