1 research outputs found
Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition
Historically, researchers in the field have spent a great deal of effort to
create image representations that have scale invariance and retain spatial
location information. This paper proposes to encode equivalent temporal
characteristics in video representations for action recognition. To achieve
temporal scale invariance, we develop a method called temporal scale pyramid
(TSP). To encode temporal information, we present and compare two methods
called temporal extension descriptor (TED) and temporal division pyramid (TDP)
. Our purpose is to suggest solutions for matching complex actions that have
large variation in velocity and appearance, which is missing from most current
action representations. The experimental results on four benchmark datasets,
UCF50, HMDB51, Hollywood2 and Olympic Sports, support our approach and
significantly outperform state-of-the-art methods. Most noticeably, we achieve
65.0% mean accuracy and 68.2% mean average precision on the challenging HMDB51
and Hollywood2 datasets which constitutes an absolute improvement over the
state-of-the-art by 7.8% and 3.9%, respectively