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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
Every moment counts in action recognition. A comprehensive understanding of
human activity in video requires labeling every frame according to the actions
occurring, placing multiple labels densely over a video sequence. To study this
problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new
dataset of dense labels over unconstrained internet videos. Modeling multiple,
dense labels benefits from temporal relations within and across classes. We
define a novel variant of long short-term memory (LSTM) deep networks for
modeling these temporal relations via multiple input and output connections. We
show that this model improves action labeling accuracy and further enables
deeper understanding tasks ranging from structured retrieval to action
prediction.
